R Notebook
library(readxl)
library(clusterProfiler)clusterProfiler v4.8.2 For help: https://yulab-smu.top/biomedical-knowledge-mining-book/
If you use clusterProfiler in published research, please cite:
T Wu, E Hu, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L Zhan, X Fu, S Liu, X Bo, and G Yu. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. The Innovation. 2021, 2(3):100141
Attaching package: ‘clusterProfiler’
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filterlibrary(GOplot)Loading required package: ggplot2
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Loading required package: gridExtra
Attaching package: ‘gridExtra’
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Loading required package: RColorBrewerlibrary(DOSE)DOSE v3.26.1 For help: https://yulab-smu.top/biomedical-knowledge-mining-book/
If you use DOSE in published research, please cite:
Guangchuang Yu, Li-Gen Wang, Guang-Rong Yan, Qing-Yu He. DOSE: an R/Bioconductor package for Disease Ontology Semantic and Enrichment analysis. Bioinformatics 2015, 31(4):608-609library(topGO)Loading required package: graph
Loading required package: GO.db
Loading required package: AnnotationDbi
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Loading required package: SparseM
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backsolve
groupGOTerms: GOBPTerm, GOMFTerm, GOCCTerm environments built.
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memberslibrary(circlize)========================================
circlize version 0.4.15
CRAN page: https://cran.r-project.org/package=circlize
Github page: https://github.com/jokergoo/circlize
Documentation: https://jokergoo.github.io/circlize_book/book/
If you use it in published research, please cite:
Gu, Z. circlize implements and enhances circular visualization
in R. Bioinformatics 2014.
This message can be suppressed by:
suppressPackageStartupMessages(library(circlize))
========================================
Attaching package: ‘circlize’
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degreelibrary("pheatmap")
library("ggsci")
library("ggplot2")
library("gridExtra")
library("survival")
library("survminer")Loading required package: ggpubr
Attaching package: ‘survminer’
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myelomalibrary(ggpubr)
library(rstpm2)
library(corrplot)corrplot 0.92 loadedlibrary(Hmisc)Registered S3 method overwritten by 'htmlwidgets':
method from
print.htmlwidget tools:rstudio
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format.pval, unitslibrary(cowplot)
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get_legendlibrary(reshape)
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stamp
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expand, renamelibrary(ggbreak)ggbreak v0.1.2
If you use ggbreak in published research, please cite the following paper:
S Xu, M Chen, T Feng, L Zhan, L Zhou, G Yu. Use ggbreak to effectively utilize plotting space to deal with large datasets and outliers.
Frontiers in Genetics. 2021, 12:774846. doi: 10.3389/fgene.2021.774846 library(ggrepel)GiniClin <- read_excel("~/Project/TCR_Summary/Writing/SumSup.xlsx", sheet = "Gini_Clinic")
GiniClin <- as.data.frame(GiniClin, row.names = GiniClin$ID)
Figure 1
f1a <- ggplot(GiniClin, aes(x=GiniIndex)) +
geom_histogram(colour = "black", fill="#4dbbd5ff", bins = 50) +
theme_classic() +
theme(axis.text = element_text(size = 20)) +
labs(title = "Gini_distribution", y = "Patient count") +
scale_x_continuous(expand = c(0,0), limits = c(0, 0.8)) + scale_y_continuous(expand = c(0,0), limits = c(0,25))
f1aWarning: Removed 2 rows containing missing values (geom_bar).
qqnorm(GiniClin_tumor$GiniIndex, ylab = 'GiniIndex')
qqline(GiniClin_tumor$GiniIndex) # å¢žåŠ è¶‹åŠ¿ç›´çº¿ï¼Œåˆ©äºŽæ¯”è¾ƒ
# H0 is that the data fit normal distribution
shapiro.test(GiniClin_tumor$GiniIndex)
Shapiro-Wilk normality test
data: GiniClin_tumor$GiniIndex
W = 0.92421, p-value = 4.017e-08GiniClin_AcSq <- GiniClin[GiniClin$histology_Hans_B_WHO_2015 %in% c("AC","SqCC"), ]
f1a_2 <- ggplot(GiniClin_AcSq, aes(x=GiniIndex, fill = histology_Hans_B_WHO_2015)) +
geom_histogram(color = "black", position = "identity", bins = 50, size = 0.3) +
theme_classic() +
theme(axis.text = element_text(size = 20, colour = "black"), legend.position = c(0.8,0.8)) +
labs(title = "Gini_distribution", fill = "Histology") +
scale_fill_manual(values = alpha(c("#F8766D", "#00BFC4"), 0.75)) +
scale_x_continuous(expand = c(0,0), limits = c(0,0.85)) + scale_y_continuous(expand = c(0,0), limits = c(0,17), breaks = c(seq(0,15,5)))
f1a_2Warning: Removed 4 rows containing missing values (geom_bar).
GiniClin_tumor <- read_excel("~/Project/TCR_Summary/Writing/SumSup.xlsx", sheet = "Gini_Clinic_tumor")
GiniClin_tumor <- as.data.frame(GiniClin_tumor, row.names = GiniClin_tumor$ID)Box plot
# histology
f1b_3 <- ggplot(GiniClin_tumor, aes(x = histology_Hans_B_WHO_2015, y = GiniIndex, fill=histology_Hans_B_WHO_2015)) +
geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
theme_classic() +
#coord_flip() +
scale_fill_manual(values = c("#F8766D", "#999999", "#00BF7D","#00B0F6","#A3A500", "#00BFC4")) +
labs(title = "Histology", x = "Histology") +
theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
stat_compare_means(paired = NULL)
f1b_3
GiniClin_tumor[GiniClin_tumor$gender == "M" ,"gender"] <- "Male"
GiniClin_tumor[GiniClin_tumor$gender == "F" ,"gender"] <- "Female"
f1b_4 <- ggplot(GiniClin_tumor, aes(x = gender, y = GiniIndex, fill=gender)) +
geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
theme_classic() +
# coord_flip() +
# scale_fill_npg() +
labs(title = "Gender", x = "Sex") +
theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
stat_compare_means(paired = NULL)
f1b_4
# WHO PS
GiniClin_tumor[GiniClin_tumor$WHO_PS == 0 ,"WHO_PS_group"] <- 0
GiniClin_tumor[GiniClin_tumor$WHO_PS > 0 ,"WHO_PS_group"] <- "> 0"
f1b_6 <- ggplot(GiniClin_tumor, aes(x = WHO_PS_group, y = GiniIndex, fill=WHO_PS_group)) +
geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
theme_classic() +
# coord_flip() +
scale_fill_manual(values = c("#F8766D", "#00BFC4")) +
labs(title = "WHO performance status", x = "WHO performance rank") +
theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
stat_compare_means(paired = FALSE)
f1b_6
AC/SqCC
ggplot(GiniClin_tumor[GiniClin_tumor$histology_Hans_B_WHO_2015 == "AC", ], aes(x = stageGroup, y = GiniIndex, fill=stageGroup)) +
geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
theme_classic() +
# coord_flip() +
# scale_color_npg() +
labs(title = "Stage", x = "Stage") +
theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
stat_compare_means(paired = NULL)
ggplot(GiniClin_tumor[GiniClin_tumor$histology_Hans_B_WHO_2015 == "SqCC", ], aes(x = stageGroup, y = GiniIndex, fill=stageGroup)) +
geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
theme_classic() +
# coord_flip() +
# scale_color_npg() +
labs(title = "Stage", x = "Stage") +
theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
stat_compare_means(paired = NULL)
ggplot(GiniClin_tumor[GiniClin_tumor$histology_Hans_B_WHO_2015 == "AC", ], aes(x = WHO_PS_group, y = GiniIndex, fill=WHO_PS_group)) +
geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
theme_classic() +
# coord_flip() +
scale_fill_manual(values = c("#F8766D", "#00BFC4")) +
labs(title = "WHO performance status", x = "WHO performance rank") +
theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
stat_compare_means(paired = FALSE)
ggplot(GiniClin_tumor[GiniClin_tumor$histology_Hans_B_WHO_2015 == "SqCC", ], aes(x = WHO_PS_group, y = GiniIndex, fill=WHO_PS_group)) +
geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
theme_classic() +
# coord_flip() +
scale_fill_manual(values = c("#F8766D", "#00BFC4")) +
labs(title = "WHO performance status", x = "WHO performance rank") +
theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
stat_compare_means(paired = FALSE)
Figure 2
library("survival")
library("survminer")
Attaching package: ‘survminer’
The following object is masked from ‘package:survival’:
myelomaFigure2A: KM plot
All patient
GiniClin_tumor[GiniClin_tumor$GiniIndex >= 0.25431, "giniGroup"] <- "Above median"
GiniClin_tumor[GiniClin_tumor$GiniIndex < 0.25431, "giniGroup"] <- "Under median"
GiniClin_tumor$giniGroup = factor(GiniClin_tumor$giniGroup, levels = c("Above median", "Under median"))# construct the object
fig2test <- survfit(Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~ giniGroup, data = GiniClin_tumor)
print(fig2test)Call: survfit(formula = Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~
giniGroup, data = GiniClin_tumor)
n events median 0.95LCL 0.95UCL
giniGroup=Under median 91 46 4.99 3.55 NA
giniGroup=Above median 91 40 NA 4.80 NAsummary(fig2test)$table records n.max n.start events rmean se(rmean) median 0.95LCL 0.95UCL
giniGroup=Under median 91 91 91 46 3.746049 0.1583925 4.985626 3.545517 NA
giniGroup=Above median 91 91 91 40 3.680266 0.1885308 NA 4.799452 NAfig2a_all
Warning messages:
1: In overlay(...) :
reverting 'unnamed.chunk.label' to 'unnamed-chunk' for duration of render
2: In overlay(...) :
reverting 'unnamed.chunk.label' to 'unnamed-chunk' for duration of render
3: In overlay(...) :
reverting 'unnamed.chunk.label' to 'unnamed-chunk' for duration of render
fig2a_continue.cox <- coxph(Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~ GiniIndex, data = GiniClin_tumor)
fig2a_continue.cox
Call:
coxph(formula = Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~
GiniIndex, data = GiniClin_tumor)
coef exp(coef) se(coef) z p
GiniIndex -0.5778 0.5611 1.0416 -0.555 0.579
Likelihood ratio test=0.31 on 1 df, p=0.5749
n= 182, number of events= 86 ggsurvplot(survfit(fig2a_continue.cox, data=GiniClin_tumor), color = "#2E9FDF",ggtheme = theme_minimal())Warning: Now, to change color palette, use the argument palette= '#2E9FDF' instead of color = '#2E9FDF'
Time split surv
fig2_timespl.cox <- coxph(Surv(tstart, OS_years_5_years_trunk_20190329, event==1) ~ giniGroup_nr:strata(tgroup), data=fig2_timespl, ties="efron")
summary(fig2_timespl.cox)
Call:
coxph(formula = Surv(tstart, OS_years_5_years_trunk_20190329,
event == 1) ~ giniGroup_nr:strata(tgroup), data = fig2_timespl,
ties = "efron")
n= 323, number of events= 85
coef exp(coef) se(coef) z Pr(>|z|)
giniGroup_nr:strata(tgroup)tgroup=1 0.5457 1.7258 0.3292 1.658 0.0974 .
giniGroup_nr:strata(tgroup)tgroup=2 -0.7484 0.4731 0.3150 -2.376 0.0175 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
giniGroup_nr:strata(tgroup)tgroup=1 1.7258 0.5794 0.9052 3.2901
giniGroup_nr:strata(tgroup)tgroup=2 0.4731 2.1137 0.2552 0.8772
Concordance= 0.58 (se = 0.027 )
Likelihood ratio test= 8.86 on 2 df, p=0.01
Wald test = 8.39 on 2 df, p=0.02
Score (logrank) test = 8.73 on 2 df, p=0.01cox.zph(fig2_timespl.cox)
chisq df p
giniGroup_nr:strata(tgroup) 1.22 2 0.54
GLOBAL 1.22 2 0.54fig2_mod_tvc <- stpm2(Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329 == 1) ~ giniGroup_nr, data = GiniClin_tumor, df=3, tvc=list(giniGroup_nr=1))Warning in gsm(formula = Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329 == :
Some event times <= 0plot(fig2_mod_tvc, newdata = data.frame(giniGroup_nr = 0), type = "hazard", xlim=c(0.5,5), ylim=c(0,0.2), ylab = "Hazard", xlab = "Time", lwd = 2, ci = FALSE)Warning in rug(eventTimes, col = line.col) :
some values will be clippedplot(fig2_mod_tvc, newdata = data.frame(giniGroup_nr = 1), type = "hazard", line.col=2, ci=FALSE, lwd = 2, add=TRUE)
Calculate best cutoff
fig2.cut <- surv_cutpoint(GiniClin_tumor,
time = "OS_years_5_years_trunk_20190329",
event = "status_5_years_trunk_20190329",
variables = "GiniIndex")
summary(fig2.cut)plot(fig2.cut, "GiniIndex", palette = "npg")$GiniIndexWarning message:
In overlay(...) :
reverting 'unnamed.chunk.label' to 'unnamed-chunk' for duration of render
Calculate AC and SqCC
plot(fig2.cut_AC, "GiniIndex", palette = "npg")$GiniIndex
KM without censoring
fig2_NoCensor_test <- survfit(Surv(OS_years_20190329, status_out_20190329) ~ giniGroup, data = GiniClin_tumor)
print(fig2_NoCensor_test)Call: survfit(formula = Surv(OS_years_20190329, status_out_20190329) ~
giniGroup, data = GiniClin_tumor)
n events median 0.95LCL 0.95UCL
giniGroup=Under median 91 66 5.17 3.55 7.59
giniGroup=Above median 91 53 6.66 4.80 NAsummary(fig2_NoCensor_test)$table records n.max n.start events rmean se(rmean) median 0.95LCL 0.95UCL
giniGroup=Under median 91 91 91 66 6.482247 0.4825654 5.169062 3.545517 7.59206
giniGroup=Above median 91 91 91 53 7.380460 0.5523298 6.661191 4.799452 NA
fig2_NoCensor.cut <- surv_cutpoint(GiniClin_tumor,
time = "OS_years_20190329",
event = "status_out_20190329",
variables = "GiniIndex")
summary(fig2_NoCensor.cut)plot(fig2_NoCensor.cut, "GiniIndex", palette = "npg")$GiniIndex
ggsurvplot(fig2_NoCensor.cut.fit,
pval = TRUE,
palette = c("#e64b35", "#4dbbd5"),
break.time.by = 2,
ggtheme = theme_classic(),
legend.labs = c("Above median", "Under median"),
risk.table = "abs_pct",
risk.table.y.text = FALSE,
xlab = "Time (years)",
title = "giniSurv_without_censoring",
axes.offset = FALSE,
risk.table.fontsize = 2.5,
legend = c(0.9,0.9),
risk.table.title = "Number at risk by time: n (%)",
font.main = c())
AC patient
GiniClin_tumor_Ac = GiniClin_tumor[GiniClin_tumor$histology_Hans_B_WHO_2015 == "AC", ]
fig2ACtest <- survfit(Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~ giniGroup, data = GiniClin_tumor_Ac)
print(fig2ACtest)Call: survfit(formula = Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~
giniGroup, data = GiniClin_tumor_Ac)
n events median 0.95LCL 0.95UCL
giniGroup=Above median 50 20 NA 4.80 NA
giniGroup=Under median 57 31 4.07 2.95 NAprint("====")[1] "===="summary(fig2ACtest)$table records n.max n.start events rmean se(rmean) median 0.95LCL 0.95UCL
giniGroup=Above median 50 50 50 20 3.889363 0.2358844 NA 4.799452 NA
giniGroup=Under median 57 57 57 31 3.523674 0.2151269 4.071184 2.945927 NA
without sensoring
fig2AC_NoCensor_test <- survfit(Surv(OS_years_20190329, status_out_20190329) ~ giniGroup, data = GiniClin_tumor[GiniClin_tumor$histology_Hans_B_WHO_2015 == "AC", ])
print(fig2AC_NoCensor_test)Call: survfit(formula = Surv(OS_years_20190329, status_out_20190329) ~
giniGroup, data = GiniClin_tumor[GiniClin_tumor$histology_Hans_B_WHO_2015 ==
"AC", ])
n events median 0.95LCL 0.95UCL
giniGroup=Under median 57 40 4.07 2.95 7.59
giniGroup=Above median 50 28 7.31 4.80 NAsummary(fig2AC_NoCensor_test)$table records n.max n.start events rmean se(rmean) median 0.95LCL 0.95UCL
giniGroup=Under median 57 57 57 40 6.139774 0.6418922 4.071184 2.945927 7.59206
giniGroup=Above median 50 50 50 28 7.749610 0.7251560 7.305955 4.799452 NAggsurvplot(fig2AC_NoCensor_test,
pval = TRUE,
palette = c("#e64b35", "#4dbbd5"),
break.time.by = 1,
ggtheme = theme_classic(),
legend.labs = c("Above median", "Under median"),
risk.table = "abs_pct",
risk.table.y.text = FALSE,
xlab = "Time (years)",
title = "giniSurv_without_censoring",
axes.offset = FALSE,
risk.table.fontsize = 2.5,
legend = c(0.9,0.9),
risk.table.title = "Number at risk by time: n (%)",
font.main = c())
SqCC patient
GiniClin_tumor_SqCC = GiniClin_tumor[GiniClin_tumor$histology_Hans_B_WHO_2015 == "SqCC", ]
fig2SqCCtest_2 <- survfit(Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~ giniGroup, data = GiniClin_tumor_SqCC)
print(fig2SqCCtest)Call: survfit(formula = Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~
giniGroup, data = GiniClin_SqCC)
n events median 0.95LCL 0.95UCL
giniGroup=Above median 41 21 4.86 3.20 NA
giniGroup=Under median 32 12 NA 4.39 NAprint("====")[1] "===="summary(fig2SqCCtest)$table records n.max n.start events rmean se(rmean) median 0.95LCL 0.95UCL
giniGroup=Above median 41 41 41 21 3.366955 0.2968385 4.856947 3.195072 NA
giniGroup=Under median 32 32 32 12 4.213723 0.2114153 NA 4.388775 NA
fig2_SqCC_timespl <- survSplit(Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329 == 1) ~ ., data=GiniClin_tumor_SqCC, cut=2, episode = "tgroup")
fig2_SqCC_timespl.cox <- coxph(Surv(tstart, OS_years_5_years_trunk_20190329, event==1) ~ giniGroup:strata(tgroup), data=fig2_SqCC_timespl, ties="efron")
summary(fig2_SqCC_timespl.cox)Call:
coxph(formula = Surv(tstart, OS_years_5_years_trunk_20190329,
event == 1) ~ giniGroup:strata(tgroup), data = fig2_SqCC_timespl,
ties = "efron")
n= 109, number of events= 28
coef exp(coef) se(coef) z Pr(>|z|)
giniGroupAbove median:strata(tgroup)tgroup=1 1.7134 5.5476 0.7602 2.254 0.0242 *
giniGroupUnder median:strata(tgroup)tgroup=1 NA NA 0.0000 NA NA
giniGroupAbove median:strata(tgroup)tgroup=2 -0.9789 0.3757 0.6018 -1.627 0.1038
giniGroupUnder median:strata(tgroup)tgroup=2 NA NA 0.0000 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
giniGroupAbove median:strata(tgroup)tgroup=1 5.5476 0.1803 1.2503 24.615
giniGroupUnder median:strata(tgroup)tgroup=1 NA NA NA NA
giniGroupAbove median:strata(tgroup)tgroup=2 0.3757 2.6615 0.1155 1.222
giniGroupUnder median:strata(tgroup)tgroup=2 NA NA NA NA
Concordance= 0.652 (se = 0.039 )
Likelihood ratio test= 10.24 on 2 df, p=0.006
Wald test = 7.73 on 2 df, p=0.02
Score (logrank) test = 9.3 on 2 df, p=0.01without sensoring
fig2SqCC_NoCensor_test <- survfit(Surv(OS_years_20190329, status_out_20190329) ~ giniGroup, data = GiniClin_tumor[GiniClin_tumor$histology_Hans_B_WHO_2015 == "SqCC", ])
print(fig2SqCC_NoCensor_test)Call: survfit(formula = Surv(OS_years_20190329, status_out_20190329) ~
giniGroup, data = GiniClin_tumor[GiniClin_tumor$histology_Hans_B_WHO_2015 ==
"SqCC", ])
n events median 0.95LCL 0.95UCL
giniGroup=Under median 26 21 6.38 3.50 8.97
giniGroup=Above median 36 22 7.05 1.96 NAsummary(fig2SqCC_NoCensor_test)$table records n.max n.start events rmean se(rmean) median 0.95LCL 0.95UCL
giniGroup=Under median 26 26 26 21 6.777249 0.7798128 6.384668 3.501711 8.969199
giniGroup=Above median 36 36 36 22 7.032550 0.8931418 7.052704 1.960301 NAggsurvplot(fig2SqCC_NoCensor_test,
pval = TRUE,
palette = c("#e64b35", "#4dbbd5"),
break.time.by = 1,
ggtheme = theme_classic(),
legend.labs = c("Above median", "Under median"),
risk.table = "abs_pct",
risk.table.y.text = FALSE,
xlab = "Time (years)",
title = "giniSurv_without_censoring",
axes.offset = FALSE,
risk.table.fontsize = 2.5,
legend = c(0.9,0.9),
risk.table.title = "Number at risk by time: n (%)",
font.main = c())
Table: Cox regression
Single variate
fig2.cox <- coxph(Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~ giniGroup, data = GiniClin_tumor)
print("===")
[1] "==="fig2.coxCall:
coxph(formula = Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~
giniGroup, data = GiniClin_tumor)
coef exp(coef) se(coef) z p
giniGroupAbove median -0.1166 0.8900 0.2165 -0.539 0.59
Likelihood ratio test=0.29 on 1 df, p=0.5898
n= 182, number of events= 86 print("===")[1] "==="print(summary(fig2.cox))Call:
coxph(formula = Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~
giniGroup, data = GiniClin_tumor)
n= 182, number of events= 86
coef exp(coef) se(coef) z Pr(>|z|)
giniGroupAbove median -0.1166 0.8900 0.2165 -0.539 0.59
exp(coef) exp(-coef) lower .95 upper .95
giniGroupAbove median 0.89 1.124 0.5822 1.36
Concordance= 0.503 (se = 0.028 )
Likelihood ratio test= 0.29 on 1 df, p=0.6
Wald test = 0.29 on 1 df, p=0.6
Score (logrank) test = 0.29 on 1 df, p=0.6print("===")[1] "==="cox.zph(fig2.cox) chisq df p
giniGroup 5.22 1 0.022
GLOBAL 5.22 1 0.022# Merge the histology group except AC and SqCC to others
GiniClin_tumor[!(GiniClin_tumor$histology_Hans_B_WHO_2015 %in% c("AC","SqCC")), "Histology"] <- "Other"
GiniClin_tumor[GiniClin_tumor$histology_Hans_B_WHO_2015 == "AC", "Histology"] <- "AC"
GiniClin_tumor[GiniClin_tumor$histology_Hans_B_WHO_2015 == "SqCC", "Histology"] <- "SqCC"# Merge the current smoke and former smoke in to one group
GiniClin_tumor[GiniClin_tumor$smoke %in% c("Current smoke","Former smoke"), "smokeGroup"] <- "Current/Former"
GiniClin_tumor[GiniClin_tumor$smoke == "Never smoke", "smokeGroup"] <- "Never"# Construct levels of each parameter
GiniClin_tumor$stageGroup = factor(GiniClin_tumor$stageGroup, levels = c("Low stage", "High stage"))
GiniClin_tumor$gender = factor(GiniClin_tumor$gender, levels = c("Male", "Female"))
GiniClin_tumor$Histology = factor(GiniClin_tumor$Histology, levels = c("AC", "SqCC", "Other"))
GiniClin_tumor$ageGroup = factor(GiniClin_tumor$ageGroup, levels = c("Under 70", "Above 70"))
GiniClin_tumor$smokeGroup = factor(GiniClin_tumor$smokeGroup, levels = c("Never","Current/Former"))Multi-variate cox regression
fig2.multicox <- coxph(Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~ giniGroup + ageGroup + gender + smokeGroup + Histology + stageGroup, data = GiniClin_tumor)
summary(fig2.multicox)Call:
coxph(formula = Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~
giniGroup + ageGroup + gender + smokeGroup + Histology +
stageGroup, data = GiniClin_tumor)
n= 182, number of events= 86
coef exp(coef) se(coef) z Pr(>|z|)
giniGroupAbove median -0.04817 0.95298 0.22266 -0.216 0.82874
ageGroupAbove 70 0.23155 1.26055 0.21842 1.060 0.28910
genderFemale -0.24394 0.78354 0.22166 -1.101 0.27110
smokeGroupCurrent/Former 0.23605 1.26624 0.39017 0.605 0.54518
HistologySqCC -0.18836 0.82832 0.24177 -0.779 0.43592
HistologyOther -0.20626 0.81362 0.44507 -0.463 0.64305
stageGroupHigh stage 0.58274 1.79095 0.22201 2.625 0.00867 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
giniGroupAbove median 0.9530 1.0493 0.6160 1.474
ageGroupAbove 70 1.2605 0.7933 0.8216 1.934
genderFemale 0.7835 1.2763 0.5074 1.210
smokeGroupCurrent/Former 1.2662 0.7897 0.5894 2.720
HistologySqCC 0.8283 1.2073 0.5157 1.330
HistologyOther 0.8136 1.2291 0.3401 1.947
stageGroupHigh stage 1.7909 0.5584 1.1591 2.767
Concordance= 0.616 (se = 0.028 )
Likelihood ratio test= 10.84 on 7 df, p=0.1
Wald test = 10.91 on 7 df, p=0.1
Score (logrank) test = 11.15 on 7 df, p=0.1Without adjuvent treatment
# Subset the group without adjuvent treatment
GiniClin_noTreat = subset(GiniClin_tumor, Annette_adjuvant == 0)
GiniClin_noTreat = as.data.frame(GiniClin_noTreat)
GiniClin_noTreat$giniGroup = factor(GiniClin_noTreat$giniGroup, levels = c("Above median", "Under median"))
# construct the object
fig8test <- survfit(Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~ giniGroup, data = GiniClin_noTreat)
print(fig8test)Call: survfit(formula = Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~
giniGroup, data = GiniClin_noTreat)
n events median 0.95LCL 0.95UCL
giniGroup=Above median 48 18 NA NA NA
giniGroup=Under median 37 17 NA 3.55 NAsummary(fig8test)$table records n.max n.start events rmean se(rmean) median 0.95LCL 0.95UCL
giniGroup=Above median 48 48 48 18 3.814767 0.2603937 NA NA NA
giniGroup=Under median 37 37 37 17 3.830473 0.2522535 NA 3.545517 NA
# Subset the group without adjuvent treatment
GiniClin_Treat = subset(GiniClin_tumor, Annette_adjuvant == 1)
GiniClin_Treat = as.data.frame(GiniClin_Treat)
GiniClin_Treat$giniGroup = factor(GiniClin_Treat$giniGroup, levels = c("Above median", "Under median"))
# construct the object
fig8test_2 <- survfit(Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~ giniGroup, data = GiniClin_Treat)
print(fig8test_2)Call: survfit(formula = Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~
giniGroup, data = GiniClin_Treat)
n events median 0.95LCL 0.95UCL
giniGroup=Above median 39 19 NA 3.2 NA
giniGroup=Under median 45 23 4.82 3.5 NAsummary(fig8test_2)$table records n.max n.start events rmean se(rmean) median 0.95LCL 0.95UCL
giniGroup=Above median 39 39 39 19 3.523193 0.2922509 NA 3.195072 NA
giniGroup=Under median 45 45 45 23 3.814769 0.2146622 4.824093 3.498973 NA
Figure mutation burden
Mutation burden
mutClin <- read_excel("~/Project/TCR_Summary/Writing/SumSup.xlsx", sheet = "MutationBurdern_all_tumor")
mutClin <- as.data.frame(mutClin, row.names = mutClin$ID)
mutClin$AB_GiniIndex <- as.numeric(mutClin$AB_GiniIndex)
mutClin$Mutation_load_allmutations <- as.numeric(mutClin$Mutation_load_allmutations)
mutClin <- mutClin[intersect(rownames(mutClin), rownames(GiniClin_tumor)),]fig4a <- ggscatter(mutClin[,c("AB_GiniIndex","Mutation_load_allmutations")], x = "AB_GiniIndex", y = "Mutation_load_allmutations",
add="reg.line", add.params = list(color = "#00bfc4"),
conf.int = FALSE,
cor.coef = TRUE,
cor.coeff.args = list(method = "spearman", label.x.npc = 0.15, label.y.npc = 0.85),
ggtheme = theme_classic(),
#add = "reg.line", add.params = c(color = '#00bfc4'),
title = "Mutation burden", xlab = "Gini Index", ylab = "Mutation load of all mutations",
font.tickslab = c(size=15, color = "black"), font.main = 20,font.y = 15, font.x = 15) +
scale_y_continuous(expand = c(0,0), limits = c(0, 130), breaks = c(seq(0,120,30))) +
scale_x_continuous(expand = c(0,0), limits = c(0, 0.75), breaks = c(seq(0,0.6,0.2)))
fig4a
`geom_smooth()` using formula 'y ~ x'
Mutation
for (mutGene in colnames(mutGini[,(3:84)])){
WT_buff <- mutGini[mutGini[,mutGene] == "WT","GiniIndex"]
Mut_buff <- mutGini[mutGini[,mutGene] == "Mutated","GiniIndex"]
mutRes <- wilcox.test(WT_buff,Mut_buff)
mutTest <- c(mutTest,c(mutGene,mutRes$statistic,mutRes$p.value))
}
Error in h(simpleError(msg, call)) :
error in evaluating the argument 'x' in selecting a method for function 'colnames': undefined columns selectedEGFR
APC
f4c.apc <- ggplot(mutGini, aes(x = APC, y = GiniIndex, fill=APC)) +
geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
theme_classic() +
# coord_flip() +
scale_fill_manual(values = c("#FFE5D4", "#E6EBE0")) +
labs(title = "APC", x = NULL) +
theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
stat_compare_means(paired = FALSE)
f4c.apc
TP53
f4c.tp53 <- ggplot(mutGini, aes(x = TP53, y = GiniIndex, fill=TP53)) +
geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
theme_classic() +
# coord_flip() +
scale_fill_manual(values = c("#FFE5D4", "#E6EBE0")) +
labs(title = "TP53", x = NULL) +
theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
stat_compare_means(paired = FALSE)
f4c.tp53
ARID1A
f4c.arid1a <- ggplot(mutGini, aes(x = ARID1A, y = GiniIndex, fill=ARID1A)) +
geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
theme_classic() +
# coord_flip() +
scale_fill_manual(values = c("#FFE5D4", "#E6EBE0")) +
labs(title = "ARID1A", x = NULL) +
theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
stat_compare_means(paired = FALSE)
f4c.arid1a
EPHB6
f4c.ephb6 <- ggplot(mutGini, aes(x = EPHB6, y = GiniIndex, fill=EPHB6)) +
geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
theme_classic() +
# coord_flip() +
scale_fill_manual(values = c("#FFE5D4", "#E6EBE0")) +
labs(title = "EPHB6", x = NULL) +
theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
stat_compare_means(paired = FALSE)
f4c.ephb6
CSMD3
f4c.csmd3 <- ggplot(mutGini, aes(x = CSMD3, y = GiniIndex, fill=CSMD3)) +
geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
theme_classic() +
# coord_flip() +
scale_fill_manual(values = c("#FFE5D4", "#E6EBE0")) +
labs(title = "CSMD3", x = NULL) +
theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20, hjust = 0.5)) +
#, panel.border = element_rect(color = "black", size = 1, fill = NA)
stat_compare_means(paired = FALSE)
f4c.csmd3
Combine
ggarrange(f4c.egfr, f4c.apc, f4c.tp53, f4c.arid1a, f4c.ephb6, f4c.csmd3, ncol = 2, nrow = 3, align = "hv")
Figure IHC Heatmap
prepare table
ihcAnno <- merge(ihcAnno, mut_t_anno[,c("APC","ARID1A","EPHB6")],by = "row.names", all=FALSE)
Error in h(simpleError(msg, call)) :
error in evaluating the argument 'y' in selecting a method for function 'merge': undefined columns selectedihcAnno_color = list(
"Gini index" = colorRampPalette(brewer.pal(n = 10, name = "Reds"))(100),
"KRAS" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
"EGFR" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
"PIK3CA" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
"FGFR2"= c(Mutated = "#D45F51", WT = "#B7D2E8"),
"PDGFRA"= c(Mutated = "#D45F51", WT = "#B7D2E8"),
"ROS1"= c(Mutated = "#D45F51", WT = "#B7D2E8"),
"NF1" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
"STK11" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
"TP53" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
"KEAP1" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
"NFE2L2" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
"MUC16" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
"KMT2C" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
"KMT2D" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
"SMARCA4" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
"CDKN2A" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
"CREBBP" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
"CSMD3" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
"LRP1B" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
"APC" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
"ARID1A" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
"EPHB6" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
"histology" = c(AC = "#D55740", SqCC = "#4DBCD6", AdSq = "#479E88", LCC = "#415384", LCNEC = "#F49B7F", SC = "#F8BED6"),
"gender" = c(Male = "#B9E3DF", Female = "#EFC0D5"),
"smoke" = c(Current = "#D45F51", Former = "#E68840", Never = "#BCD9B2"),
"surv5years" = c(Dead = "#D45F51", Alive = "#B7D2E8")
)
Warning: n too large, allowed maximum for palette Reds is 9
Returning the palette you asked for with that many colorsfig5b <- pheatmap(t(ihcHeatT_tumor_norm2), clustering_method = "ward.D2",
clustering_distance_rows = "euclidean", clustering_distance_cols = "euclidean",
cutree_cols = 3,
#color = colorRampPalette(brewer.pal(n = 7, name = "YlGnBu"))(100),
annotation_col = ihcAnno,
annotation_colors = ihcAnno_color,
fontsize = 5,
show_colnames = F
)
fig5bfig5b.cluster <- fig5b$tree_col
fig5b.cluster.plot <- plot(fig5b.cluster, hang = -1, cex=0.6, axes=FALSE, ann=FALSE)
fig5b.cut <- cutree(fig5b.cluster, 3)
fig5b.cut_1 <- names(fig5b.cut)[fig5b.cut == 1]
fig5b.cut_2 <- names(fig5b.cut)[fig5b.cut == 2]
fig5b.cut_3 <- names(fig5b.cut)[fig5b.cut == 3]
fig5b.cut_gini <- merge(AB_Gini_anno, fig5b.cut, by = "row.names", all.y = TRUE)
fig5b.cut_gini <- as.data.frame(fig5b.cut_gini, row.names = fig5b.cut_gini$Row.names)
fig5b.cut_gini$Row.names <- NULL
fig5b.cut_gini <- rename(fig5b.cut_gini, c("y"="Cluster"))fig5b.cut_gini[fig5b.cut_gini$Cluster == 2, "Class"] <- "Immune hot"
fig5b.cut_gini[fig5b.cut_gini$Cluster == 3, "Class"] <- "Median"
fig5b.cut_gini[fig5b.cut_gini$Cluster == 1, "Class"] <- "Immune cold"
fig5b.cut_gini$Class = factor(fig5b.cut_gini$Class, levels = c("Immune hot", "Median", "Immune cold"))fig5b.cut_gini <- as.data.frame(fig5b.cut_gini)
fig5b.cut_gini2 <- rename(fig5b.cut_gini, c("Gini index"="GiniIndex"))
compare_means(GiniIndex ~ Class, data = fig5b.cut_gini2)fig5b.cut_km <- merge(fig5b.cut_gini, GiniClin_tumor[, c("OS_years_5_years_trunk_20190329", "status_5_years_trunk_20190329")], by = "row.names")
fig5b.cut_km <- as.data.frame(fig5b.cut_km, row.names = fig5b.cut_km$Row.names)
fig5b.cut_km$Row.names <- NULL
fig5b.test <- survfit(Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~ Class, data = fig5b.cut_km)
summary(fig5b.test)$tablefig5b.KM <- ggsurvplot(fig5b.test,
pval = TRUE,
palette = c("#e64b35", "#4dbbd5", "#999999"),
break.time.by = 1,
ggtheme = theme_classic(),
#legend.labs = c("Above median", "Under median"),
risk.table = "abs_pct",
risk.table.y.text = FALSE,
xlab = "Time (years)",
title = "giniSurv_Class",
xlim = c(0,5.2),
axes.offset = FALSE,
risk.table.fontsize = 2.5,
legend = c(0.9,0.9),
risk.table.title = "Number at risk by time: n (%)",
font.main = c())
fig5b.KM
fig5b.cut_clin <- merge(fig5b.cut_km, GiniClin_tumor[,c("histology_Hans_B_WHO_2015", "gender", "smoke")], by = "row.names", all.x = TRUE)
fig5b.cut_clin <- as.data.frame(fig5b.cut_clin, row.names = fig5b.cut_clin$Row.names)
fig5b.cut_clin$Row.names <- NULL
fig5b.cut_clin_AC <- fig5b.cut_clin[fig5b.cut_clin$histology_Hans_B_WHO_2015 == "AC", ]
fig5b.cut_clin_SqCC <- fig5b.cut_clin[fig5b.cut_clin$histology_Hans_B_WHO_2015 == "SqCC", ]
fig5b.cut_clin
fig5b.cut_clin[fig5b.cut_clin$`Gini index` >= 0.25431, "giniGroup"] <- "Above median"
fig5b.cut_clin[fig5b.cut_clin$`Gini index` < 0.25431, "giniGroup"] <- "Under median"
fig5b.cut_clin$giniGroup = factor(fig5b.cut_clin$giniGroup, levels = c("Above median", "Under median"))cluster without gini index
pheatmap(t(ihcHeatT_tumor_norm2[,(2:25)]), clustering_method = "ward.D2",
clustering_distance_rows = "euclidean", clustering_distance_cols = "euclidean",
cutree_cols = 3,
#color = colorRampPalette(brewer.pal(n = 7, name = "YlGnBu"))(100),
annotation_col = ihcAnno,
annotation_colors = ihcAnno_color,
fontsize = 5,
show_colnames = F
)
pheatmap(t(ihcHeatT_tumor_norm3[,grep("_S", colnames(ihcHeatT_tumor_norm3))]), clustering_method = "ward.D2",
clustering_distance_rows = "euclidean", clustering_distance_cols = "euclidean",
cutree_cols = 3,
#color = colorRampPalette(brewer.pal(n = 7, name = "YlGnBu"))(100),
annotation_col = ihcAnno,
annotation_colors = ihcAnno_color,
fontsize = 5,
show_colnames = F
)
pheatmap(t(ihcHeatT_tumor_norm3[,grep("_S", colnames(ihcHeatT_tumor_norm3))]), clustering_method = "ward.D2",
clustering_distance_rows = "euclidean", clustering_distance_cols = "euclidean",
cutree_cols = 3,
#color = colorRampPalette(brewer.pal(n = 7, name = "YlGnBu"))(100),
annotation_col = ihcAnno,
annotation_colors = ihcAnno_color,
fontsize = 5,
show_colnames = F
)
pheatmap(t(ihcHeatT_tumor_norm3[,grep("_T", colnames(ihcHeatT_tumor_norm3))]), clustering_method = "ward.D2",
clustering_distance_rows = "euclidean", clustering_distance_cols = "euclidean",
cutree_cols = 2,
#color = colorRampPalette(brewer.pal(n = 7, name = "YlGnBu"))(100),
annotation_col = ihcAnno,
annotation_colors = ihcAnno_color,
fontsize = 5,
show_colnames = F
)fig5d_T <- pheatmap(t(ihcHeatT_tumor_norm3[,grep("_T", colnames(ihcHeatT_tumor_norm3))]), clustering_method = "ward.D2",
clustering_distance_rows = "euclidean", clustering_distance_cols = "euclidean",
cutree_cols = 2,
#color = colorRampPalette(brewer.pal(n = 7, name = "YlGnBu"))(100),
annotation_col = ihcAnno,
annotation_colors = ihcAnno_color,
fontsize = 5,
show_colnames = F
)
fig5d_Tfig5d_T.cluster <- fig5d_T$tree_col
fig5d_T.cluster.plot <- plot(fig5d_T.cluster, hang = -1, cex=0.6, axes=FALSE, ann=FALSE)
fig5d_T.cut <- cutree(fig5d_T.cluster, 2)
fig5d_T.cut_1 <- names(fig5d_T.cut)[fig5d_T.cut == 1]
fig5d_T.cut_2 <- names(fig5d_T.cut)[fig5d_T.cut == 2]
fig5d_T.cut_gini <- merge(AB_Gini_anno, fig5d_T.cut, by = "row.names", all.y = TRUE)
fig5d_T.cut_gini <- as.data.frame(fig5d_T.cut_gini, row.names = fig5d_T.cut_gini$Row.names)
fig5d_T.cut_gini$Row.names <- NULL
fig5d_T.cut_gini <- rename(fig5d_T.cut_gini, c("Cluster"="y"))fig5d_T.cut_gini[fig5d_T.cut_gini$Cluster == 1, "Class"] <- "Immune cold"
fig5d_T.cut_gini[fig5d_T.cut_gini$Cluster == 2, "Class"] <- "Immune hot"
fig5d_T.cut_gini$Class = factor(fig5d_T.cut_gini$Class, levels = c("Immune hot", "Immune cold"))fig5d_T.box <- ggplot(fig5d_T.cut_gini, aes(x = Class, y = GiniIndex, fill=Class)) +
geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
theme_classic() +
# coord_flip() +
scale_fill_manual(values = c("#e64b35", "#4dbbd5")) +
labs(title = "Class", x = "Class") +
theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
stat_compare_means(paired = NULL)
fig5d_T.box
fig5d_T.cut_km <- merge(fig5d_T.cut_gini, GiniClin_tumor[, c("OS_years_5_years_trunk_20190329", "status_5_years_trunk_20190329")], by = "row.names")
fig5d_T.cut_km <- as.data.frame(fig5d_T.cut_km, row.names = fig5d_T.cut_km$Row.names)
fig5d_T.cut_km$Row.names <- NULL
fig5d_T.test <- survfit(Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~ Class, data = fig5d_T.cut_km)
summary(fig5d_T.test)$table records n.max n.start events rmean se(rmean) median 0.95LCL 0.95UCL
Class=Immune hot 39 39 39 18 3.704189 0.2816978 NA 4.388775 NA
Class=Immune cold 138 138 138 65 3.737826 0.1381891 NA 4.276523 NAggsurvplot(fig5d_T.test,
pval = TRUE,
palette = c("#e64b35", "#4dbbd5", "#999999"),
break.time.by = 1,
ggtheme = theme_classic(),
#legend.labs = c("Above median", "Under median"),
risk.table = "abs_pct",
risk.table.y.text = FALSE,
xlab = "Time (years)",
title = "giniSurv_Class",
xlim = c(0,5.2),
axes.offset = FALSE,
risk.table.fontsize = 2.5,
legend = c(0.9,0.9),
risk.table.title = "Number at risk by time: n (%)",
font.main = c())
fig5d_S.cluster <- fig5d_S$tree_col
fig5d_S.cluster.plot <- plot(fig5d_S.cluster, hang = -1, cex=0.6, axes=FALSE, ann=FALSE)
fig5d_S.cut <- cutree(fig5d_S.cluster, 3)
fig5d_S.cut_1 <- names(fig5d_S.cut)[fig5d_S.cut == 1]
fig5d_S.cut_2 <- names(fig5d_S.cut)[fig5d_S.cut == 2]
fig5d_S.cut_3 <- names(fig5d_S.cut)[fig5d_S.cut == 3]
fig5d_S.cut_gini <- merge(AB_Gini_anno, fig5d_S.cut, by = "row.names", all.y = TRUE)
fig5d_S.cut_gini <- as.data.frame(fig5d_S.cut_gini, row.names = fig5d_S.cut_gini$Row.names)
fig5d_S.cut_gini$Row.names <- NULL
fig5d_S.cut_gini <- rename(fig5d_S.cut_gini, c("Cluster"="y"))fig5d_S.cut_gini[fig5d_S.cut_gini$Cluster == 1, "Class"] <- "Immune cold"
fig5d_S.cut_gini[fig5d_S.cut_gini$Cluster == 2, "Class"] <- "Immune median"
fig5d_S.cut_gini[fig5d_S.cut_gini$Cluster == 3, "Class"] <- "Immune hot"
fig5d_S.cut_gini$Class = factor(fig5d_S.cut_gini$Class, levels = c("Immune hot", "Median", "Immune cold"))
fig5d_S.cut_km <- merge(fig5d_S.cut_gini, GiniClin_tumor[, c("OS_years_5_years_trunk_20190329", "status_5_years_trunk_20190329")], by = "row.names")
fig5d_S.cut_km <- as.data.frame(fig5d_S.cut_km, row.names = fig5d_S.cut_km$Row.names)
fig5d_S.cut_km$Row.names <- NULL
fig5d_S.test <- survfit(Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~ Class, data = fig5d_S.cut_km)
summary(fig5d_S.test)$table records n.max n.start events rmean se(rmean) median 0.95LCL 0.95UCL
Class=Immune hot 36 36 36 14 3.885352 0.2702259 NA 4.544832 NA
Class=Median 62 62 62 30 3.669780 0.2112939 NA 3.556468 NA
Class=Immune cold 79 79 79 39 3.707397 0.1864731 NA 4.010951 NAggsurvplot(fig5d_S.test,
pval = TRUE,
palette = c("#e64b35", "#4dbbd5", "#999999"),
break.time.by = 1,
ggtheme = theme_classic(),
#legend.labs = c("Above median", "Under median"),
risk.table = "abs_pct",
risk.table.y.text = FALSE,
xlab = "Time (years)",
title = "giniSurv_Class",
xlim = c(0,5.2),
axes.offset = FALSE,
risk.table.fontsize = 2.5,
legend = c(0.9,0.9),
risk.table.title = "Number at risk by time: n (%)",
font.main = c())ihcHeatT_tumor_norm4 <- rename_with(ihcHeatT_tumor_norm4, gsub("SQ1", "_"))
Error in is.factor(x) : argument "x" is missing, with no default
fig5d_ST.cut_gini[fig5d_ST.cut_gini$Cluster == 1, "Class"] <- "Immune cold"
fig5d_ST.cut_gini[fig5d_ST.cut_gini$Cluster == 2, "Class"] <- "Immune hot"
fig5d_ST.cut_gini$Class = factor(fig5d_ST.cut_gini$Class, levels = c("Immune hot", "Immune cold"))fig5d_ST.cut_gini <- as.data.frame(fig5d_ST.cut_gini)
fig5d_ST.cut_gini <- rename(fig5d_ST.cut_gini, c("GiniIndex"="Gini index"))
compare_means(GiniIndex ~ Class, data = fig5d_ST.cut_gini)
Ordered by Gini
pheatmap(t(ihcHeatT_tumor_norm4[order(ihcHeatT_tumor_norm4$GiniIndex),c(2:12)]),
#clustering_method = "ward.D2",
cluster_rows = F, cluster_cols = F,
cutree_cols = 2,
#color = colorRampPalette(brewer.pal(n = 7, name = "YlGnBu"))(100),
annotation_col = ihcAnno,
annotation_colors = ihcAnno_color,
fontsize = 8,
show_colnames = F)IHC correlation plot
fig6 <- corrplot(data.matrix(ihcCorR), method = "circle",col = rev(COL2('RdBu', 200)),
p.mat = data.matrix(ihcCorP), insig = "label_sig", sig.level = c(.001, .01, .05), pch.cex = 0.8,
tl.col = "black")
fig6ggplot(ihcCorP_melt, aes(var1, var2, fill = Corr)) +
geom_point(shape=21, colour = "black", size = 12)+
theme_classic()+
scale_fill_gradientn(colours = rev(brewer.pal(5, "Spectral")))+
labs(title = "Correlation", x = "IHC marker", y = "Position") +
theme(axis.text = element_text(size = 10, colour = "black"), axis.title = element_text(size = 10),plot.title = element_text(size=15))+
geom_text(aes(label = label), size = 5, colour = "black")
Warning: Removed 10 rows containing missing values (geom_text).
Figure 9 Lymphotrack cluster plot
L481
L481_L <- as.data.frame(L481_L)
L481_R <- as.data.frame(L481_R)
L481_all <- as.data.frame(L481_all)
L481_L["Class"] <- "Lymphotrack"
L481_R["Class"] <- "RNAseq"npj.pal = pal_npg("nrc", alpha = 0.9)(10)
simp.pal = pal_simpsons("springfield")(16)
nejm.pal = pal_nejm("default", alpha = 0.9)(8)
jama.pal = pal_jama("default",alpha = 0.9)(7)
fig9_pal = c(c("#E0E3E3FF"), npj.pal, simp.pal, nejm.pal, jama.pal)
fig9_L481 <- ggplot(L481_all, aes(x= Type, y= Fraction, fill= Clone)) +
geom_col(position = "stack", width = 0.6) +
theme_classic() +
scale_y_continuous(expand = c(0,0)) +
scale_fill_manual(values = fig9_pal) +
scale_y_break(c(40,95), scales = 0.2, ticklabels = c(95,100))
gg.gap(plot = fig9_L481,
segments = c(40,80),
ylim = c(0, 100),
tick_width = c(5, 10),
rel_heights = c(0.3, 0, 0.1)
)
ggplot(L481_L, aes(x=Class, y= Fraction, fill= reorder(Lymphotrack, -Fraction))) +
geom_col(position = "stack", width = 0.6) +
theme_classic() +
scale_fill_manual(values = fig9_pal) +
scale_y_break(c(7,92), scales = 0.2, ticklabels = c(95,100), expand = FALSE) +
scale_y_continuous(expand = c(0,0), breaks = c(0,2,4,6))
ggplot(L481_R, aes(x=Class, y= Fraction, fill= reorder(RNAseq, -Fraction))) +
geom_col(position = "stack", width = 0.6) +
theme_classic() +
scale_fill_manual(values = fig9_pal) +
scale_y_break(c(40,92), scales = 0.2, ticklabels = c(95,100), expand = FALSE) +
scale_y_continuous(expand = c(0,0), breaks = c(0,10,20,30,40))
Fig10 Vocanol plot
fig10 <- ggplot(data = diffGene, aes(x = log2FoldChange, y = -log10(padj), color = sign)) +
geom_point(size = 1) + #Create scatter plot
scale_color_manual(values = c('red', 'gray', 'green'), limits = c('UP', 'none', 'DOWN')) + #Set the color
labs(x = 'log2 Fold Change', y = '-log10 adjust p-value', title = 'High clonality vs Low clonality', color = '') + #Set the labels
theme(plot.title = element_text(hjust = 0.5, size = 14), panel.grid = element_blank(), #Change the them and grid lines
panel.background = element_rect(color = 'black', fill = 'transparent'),
legend.key = element_rect(fill = 'transparent')) +
geom_vline(xintercept = c(-1, 1), lty = 3, color = 'black') + #Add threshold line
geom_hline(yintercept = 2, lty = 3, color = 'black') +
xlim(-12, 12) + ylim(0, 35) + #Set the curve boundary
geom_text_repel(
data = diffGene,
aes(label = label),
size = 3, segment.color = "blue", show.legned = FALSE,
point.padding = unit(0.8, "lines")
)
Warning: Ignoring unknown parameters: `show.legned`#Figure11 Phenotype
fig11
Warning: Removed 16 rows containing missing values (geom_text).
Cluster
All gini
This method start from the t cell pheno type data to check if the gini index is linked with these group. But there is also a angle that start from Gini index (25%) to analyze the t cell composition changes
phenoClin_gini3 <- phenoClin_gini2[, grep("Pan", colnames(phenoClin_gini2), invert = T)]
Error: unexpected symbol in:
"phenoClin_gini3 <- phenoClin_gini2[, grep("Pan", colnames(phenoClin_gini2), invert = T)
phenoClin_gini3"min.max.norm <- function(x){
(x - min(x, na.rm = TRUE))/(max(x, na.rm = TRUE) - min(x, na.rm = TRUE))
}
phenoClin_gini3.norm2 <- apply(phenoClin_gini3[, c(2:40)], 2, min.max.norm)
phenoClin_gini3.norm2 <- as.data.frame(phenoClin_gini3.norm2)
phenoClin_gini3.norm2 <- cbind(phenoClin_gini3[,1], phenoClin_gini3.norm2)
colnames(phenoClin_gini3.norm2)[colnames(phenoClin_gini3.norm2) == "phenoClin_gini3[, 1]"] = "GiniIndex"pheatmap(t(phenoClin_gini2[,grep("st_", colnames(phenoClin_gini2), invert = T)]), clustering_method = "ward.D2",
clustering_distance_rows = "euclidean", clustering_distance_cols = "euclidean",
cutree_cols = 2,
#color = colorRampPalette(brewer.pal(n = 7, name = "YlGnBu"))(100),
annotation_col = ihcAnno,
annotation_colors = ihcAnno_color,
fontsize = 5,
show_colnames = F)
pheatmap(t(phenoClin_gini3.norm[,c(1,grep("st_", colnames(phenoClin_gini3.norm[,c(2:16)]), invert = T)+1)]), clustering_method = "ward.D2",
clustering_distance_rows = "euclidean", clustering_distance_cols = "euclidean",
cutree_cols = 2,
#color = colorRampPalette(brewer.pal(n = 7, name = "YlGnBu"))(100),
annotation_col = ihcAnno,
annotation_colors = ihcAnno_color,
fontsize = 5,
show_colnames = F)
pheatmap(t(phenoClin_gini3.norm2[,grep("st_", colnames(phenoClin_gini3.norm2[,c(1:16)]), invert = T)]), clustering_method = "ward.D2",
clustering_distance_rows = "euclidean", clustering_distance_cols = "euclidean",
cutree_cols = 2,
#color = colorRampPalette(brewer.pal(n = 7, name = "YlGnBu"))(100),
annotation_col = ihcAnno,
annotation_colors = ihcAnno_color,
fontsize = 5,
show_colnames = F)
pheatmap(t(phenoClin_gini3.norm2[,grep("st_", colnames(phenoClin_gini3.norm2[,c(2:16)]))]), clustering_method = "ward.D2",
cluster_rows = F, clustering_distance_cols = "euclidean",
cutree_cols = 5,
#color = colorRampPalette(brewer.pal(n = 9, name = "YlOrBr"))(100),
annotation_col = ihcAnno,
annotation_colors = ihcAnno_color,
fontsize = 5,
show_colnames = F)
pheatmap(t(phenoClin_gini3.norm2[,grep("st_", colnames(phenoClin_gini3.norm2[,c(2:13)]))+1]), clustering_method = "ward.D2",
cluster_rows = F, clustering_distance_cols = "euclidean",
cutree_cols = 4,
#color = colorRampPalette(brewer.pal(n = 9, name = "YlOrBr"))(100),
annotation_col = ihcAnno,
annotation_colors = ihcAnno_color,
fontsize = 10,
show_colnames = F)
fig11c.cut_gini <- merge(phenoClin_gini3.norm2[!(rownames(phenoClin_gini3.norm2) == "L464T"),c(1,grep("st_", colnames(phenoClin_gini3.norm2[,c(1:14)])))],
fig11c.cut, by = "row.names", all.y = TRUE)
fig11c.cut_gini <- as.data.frame(fig11c.cut_gini, row.names = fig11c.cut_gini$Row.names)
fig11c.cut_gini$Row.names <- NULL
colnames(fig11c.cut_gini)[colnames(fig11c.cut_gini) == "y"] <- "CutGroup"
fig11c.cut_gini$CutGroup = factor(fig11c.cut_gini$CutGroup)ggplot(fig11c.cut_gini, aes(x = CutGroup, y = GiniIndex, fill=CutGroup)) +
geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black", notch = FALSE) +
theme_classic() +
#coord_flip() +
#scale_color_npg() +
labs(title = "Gini", x = "Group") +
theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
stat_compare_means(paired = NULL)
ggplot(fig11c.cut_gini, aes(x = CutGroup, y = s_CD4_Treg, fill=CutGroup)) +
geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black", notch = FALSE) +
theme_classic() +
#coord_flip() +
#scale_color_npg() +
labs(title = "CD4_Treg", x = "Group") +
theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
stat_compare_means(paired = NULL)
ggplot(fig11c.cut_gini.melt, aes(x = variable, y = value, fill=variable)) +
geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black", notch = FALSE) +
theme_classic() +
#coord_flip() +
scale_fill_npg(palette = c("nrc"), alpha=1) +
labs(x = "T cell phenotype") +
theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
facet_wrap(~CutGroup,ncol = 1)
Smooth line
fig11c.cut_gini.melt2 <- melt(cbind("ID"=rownames(fig11c.cut_gini),fig11c.cut_gini[,c(1:5)]), na.rm = F, id.vars = c("ID", "GiniIndex"), variable.factor =F)
fig11c.cut_gini.melt2$variable <- as.factor(fig11c.cut_gini.melt2$variable)
# Smooth value trend line
ggplot(fig11c.cut_gini.melt2, aes(x = GiniIndex, y = value)) +
geom_smooth(aes(color=variable,fill=variable), method = "loess") +
theme_classic() +
#coord_flip() +
scale_color_npg(palette = c("nrc"), alpha=1) +
scale_fill_npg(palette = c("nrc"), alpha=1) +
labs(x = "Gini coefficient") +
theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))
# Raw stacking value
ggplot(fig11c.cut_gini.melt2, aes(x = GiniIndex, y = value,fill=variable)) +
geom_area() +
theme_classic() +
#coord_flip() +
scale_color_npg(palette = c("nrc"), alpha=1) +
scale_fill_npg(palette = c("nrc"), alpha=1) +
labs(x = "Gini coefficient") +
theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))
# Raw fraction change
ggplot(fig11c.cut_gini.melt2, aes(x = GiniIndex, y = value,fill=variable)) +
geom_area(position = "fill") +
theme_classic() +
#coord_flip() +
scale_color_npg(palette = c("nrc"), alpha=0.8) +
scale_fill_npg(palette = c("nrc"), alpha=0.8) +
labs(x = "Gini coefficient") +
theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))
# smooth density value
ggplot(fig11c.cut_gini.melt2, aes(x = GiniIndex, y = value,fill=variable)) +
geom_area(fill=NA) +
stat_smooth(geom = "area", method = "loess", span = 1/3, alpha=0.4)+
theme_classic() +
#coord_flip() +
scale_color_npg(palette = c("nrc"), alpha=1) +
scale_fill_npg(palette = c("nrc"), alpha=1) +
labs(x = "Gini coefficient") +
theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))
# Smooth stacking density value
ggplot(fig11c.cut_gini.melt2, aes(x = GiniIndex, y = value)) +
stat_smooth(aes(color=variable,fill=variable), method = "loess", geom="area", position = "stack",span = 0.5) +
theme_classic() +
#coord_flip() +
scale_color_npg(palette = c("nrc"), alpha=1) +
scale_fill_npg(palette = c("nrc"), alpha=0.7) +
labs(x = "Gini coefficient") +
theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))
# Smooth fraction changes
ggplot(fig11c.cut_gini.melt2, aes(x = GiniIndex, y = value)) +
stat_smooth(aes(color=variable,fill=variable), method = "loess", geom="area", position = "fill",span = 0.3) +
theme_classic() +
#coord_flip() +
scale_color_npg(palette = c("nrc"), alpha=1) +
scale_fill_npg(palette = c("nrc"), alpha=0.7) +
labs(x = "Gini coefficient") +
theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))+
scale_x_continuous(expand = c(0,0), limits = c(0,0.8)) + scale_y_continuous(expand = c(0,0), limits = c(0,1))
# Raw fraction change of unnormalized data
phenoClin_gini3.melt <- melt(cbind("ID"=rownames(phenoClin_gini3),phenoClin_gini3[,c(1,grep("st_", colnames(phenoClin_gini3[,c(1:13)])))]),
na.rm = F, id.vars = c("ID", "GiniIndex"), variable.factor =F)
ggplot(phenoClin_gini3.melt, aes(x = GiniIndex, y = value,fill=variable)) +
geom_area(position = "fill") +
theme_classic() +
#coord_flip() +
scale_color_npg(palette = c("nrc"), alpha=0.8) +
scale_fill_npg(palette = c("nrc"), alpha=0.8) +
labs(x = "Gini coefficient") +
theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))
# Raw stacking of unnormalized data
ggplot(phenoClin_gini3.melt, aes(x = GiniIndex, y = value,fill=variable)) +
geom_area(position = "stack") +
theme_classic() +
#coord_flip() +
scale_color_npg(palette = c("nrc"), alpha=0.8) +
scale_fill_npg(palette = c("nrc"), alpha=0.8) +
labs(x = "Gini coefficient") +
theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))
# Smooth fraction change of unnormalized data
ggplot(phenoClin_gini3.melt, aes(x = GiniIndex, y = value)) +
stat_smooth(aes(color=variable,fill=variable), method = "loess", geom="area", position = "fill",span = 0.3) +
theme_classic() +
#coord_flip() +
scale_color_npg(palette = c("nrc"), alpha=1) +
scale_fill_npg(palette = c("nrc"), alpha=0.7) +
labs(x = "Gini coefficient") +
theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))+
scale_y_continuous(expand = c(0,0), limits = c(0,1))
# Smooth stacking unnormalized data
ggplot(phenoClin_gini3.melt, aes(x = GiniIndex, y = value)) +
stat_smooth(aes(color=variable,fill=variable), method = "loess", geom="area", position = "stack",span = 0.3) +
theme_classic() +
#coord_flip() +
scale_color_npg(palette = c("nrc"), alpha=1) +
scale_fill_npg(palette = c("nrc"), alpha=0.7) +
labs(x = "Gini coefficient") +
theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))
fig11_gini <- ggplot(fig11c.cut_gini.melt2, aes(x = GiniIndex, y = 1)) + geom_vline(xintercept=fig11c.cut_gini.melt2[fig11c.cut_gini.melt2$variable == "st_CD4_Eff", "GiniIndex"]) + clean_theme(panel.background = element_blank())
Error in clean_theme(panel.background = element_blank()) :
unused argument (panel.background = element_blank())Ordered by gini
pheatmap(t(phenoClin_gini3.norm2[order(phenoClin_gini3.norm2$GiniIndex),c(2:40)]),
clustering_method = "ward.D2",
cluster_rows = F, clustering_distance_cols = "euclidean",
cutree_cols = 4,
#color = colorRampPalette(brewer.pal(n = 9, name = "YlOrBr"))(100),
annotation_col = ihcAnno,
annotation_colors = ihcAnno_color,
fontsize = 5,
show_colnames = F)
pheatmap(t(phenoClin_gini3.norm2[order(phenoClin_gini3.norm2$GiniIndex),c(17:31)]),
clustering_method = "ward.D2",
cluster_rows = F, cluster_cols = F,
cutree_cols = 4,
#color = colorRampPalette(brewer.pal(n = 9, name = "YlOrBr"))(100),
annotation_col = ihcAnno,
annotation_colors = ihcAnno_color,
fontsize = 5,
show_colnames = F)High gini group
fig11d.cut_gini <- merge(phenoClin_gini3.norm2[!(rownames(phenoClin_gini3.norm2) == "L464T"),c(1,grep("st_", colnames(phenoClin_gini3.norm2[,c(1:14)])))],
fig11d.cut, by = "row.names", all.y = TRUE)
fig11d.cut_gini <- as.data.frame(fig11d.cut_gini, row.names = fig11d.cut_gini$Row.names)
fig11d.cut_gini$Row.names <- NULL
colnames(fig11d.cut_gini)[colnames(fig11d.cut_gini) == "y"] <- "CutGroup"
fig11d.cut_gini$CutGroup = factor(fig11d.cut_gini$CutGroup)fig11d.cut_gini.melt <- melt(fig11d.cut_gini, na.rm = F)
Using CutGroup as id variables# Raw stacking change of unnormalized data (Gini high group)
ggplot(phenoClin_gini3.melt[phenoClin_gini3.melt$GiniIndex >= 0.25431,], aes(x = GiniIndex, y = value,fill=variable)) +
geom_area(position = "stack") +
theme_classic() +
#coord_flip() +
scale_color_npg(palette = c("nrc"), alpha=0.8) +
scale_fill_npg(palette = c("nrc"), alpha=0.8) +
labs(x = "Gini coefficient") +
theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))
# Smoothed stacking change of unnormalized data (Gini high group)
ggplot(phenoClin_gini3.melt[phenoClin_gini3.melt$GiniIndex >= 0.25431,], aes(x = GiniIndex, y = value)) +
stat_smooth(aes(color=variable,fill=variable), method = "loess", geom="area", position = "stack",span = 0.3) +
theme_classic() +
#coord_flip() +
scale_color_npg(palette = c("nrc"), alpha=1) +
scale_fill_npg(palette = c("nrc"), alpha=0.7) +
labs(x = "Gini coefficient") +
theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))
# Raw fraction change of unnormalized data (Gini high group)
ggplot(phenoClin_gini3.melt[phenoClin_gini3.melt$GiniIndex >= 0.25431,], aes(x = GiniIndex, y = value,fill=variable)) +
geom_area(position = "fill") +
theme_classic() +
#coord_flip() +
scale_color_npg(palette = c("nrc"), alpha=0.8) +
scale_fill_npg(palette = c("nrc"), alpha=0.8) +
labs(x = "Gini coefficient") +
theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))
# Smoothed fraction change of unnormalized data (Gini high group)
ggplot(phenoClin_gini3.melt[phenoClin_gini3.melt$GiniIndex >= 0.25431,], aes(x = GiniIndex, y = value)) +
stat_smooth(aes(color=variable,fill=variable), method = "loess", geom="area", position = "fill",span = 0.3) +
theme_classic() +
#coord_flip() +
scale_color_npg(palette = c("nrc"), alpha=1) +
scale_fill_npg(palette = c("nrc"), alpha=0.7) +
labs(x = "Gini coefficient") +
theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))
# Raw stacking value (Gini high group)
ggplot(fig11c.cut_gini.melt2[fig11c.cut_gini.melt2$GiniIndex >= 0.25431,], aes(x = GiniIndex, y = value,fill=variable)) +
geom_area() +
theme_classic() +
#coord_flip() +
scale_color_npg(palette = c("nrc"), alpha=1) +
scale_fill_npg(palette = c("nrc"), alpha=1) +
labs(x = "Gini coefficient") +
theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))
# Raw fraction value (Gini high group)
ggplot(fig11c.cut_gini.melt2[fig11c.cut_gini.melt2$GiniIndex >= 0.25431,], aes(x = GiniIndex, y = value,fill=variable)) +
geom_area(position = "fill") +
theme_classic() +
#coord_flip() +
scale_color_npg(palette = c("nrc"), alpha=0.8) +
scale_fill_npg(palette = c("nrc"), alpha=0.8) +
labs(x = "Gini coefficient") +
theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))
# Smoothed stacking change (Gini high group)
ggplot(fig11c.cut_gini.melt2[fig11c.cut_gini.melt2$GiniIndex >= 0.25431,], aes(x = GiniIndex, y = value)) +
stat_smooth(aes(color=variable,fill=variable), method = "loess", geom="area", position = "stack",span = 0.5) +
theme_classic() +
#coord_flip() +
scale_color_npg(palette = c("nrc"), alpha=1) +
scale_fill_npg(palette = c("nrc"), alpha=0.7) +
labs(x = "Gini coefficient") +
theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))
# Smoothed fraction change (Gini high group)
ggplot(fig11c.cut_gini.melt2[fig11c.cut_gini.melt2$GiniIndex >= 0.25431,], aes(x = GiniIndex, y = value)) +
stat_smooth(aes(color=variable,fill=variable), method = "loess", geom="area", position = "fill",span = 0.5) +
theme_classic() +
#coord_flip() +
scale_color_npg(palette = c("nrc"), alpha=1) +
scale_fill_npg(palette = c("nrc"), alpha=0.7) +
labs(x = "Gini coefficient") +
theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))
Fig12
PhenoAll <- read_excel("~/Project/BOMI2/bomi2_phenotyped_densities_sep.xlsx", sheet = "All")
PhenoAllNoCK <- read_excel("~/Project/BOMI2/bomi2_phenotyped_densities_sep.xlsx", sheet = "noCK")
PhenoTIL <- read_excel("~/Project/BOMI2/bomi2_phenotyped_densities_sep.xlsx", sheet = "TIL")
PhenoNK <- read_excel("~/Project/BOMI2/bomi2_phenotyped_densities_sep.xlsx", sheet = "NK")
PhenoAPC <- read_excel("~/Project/BOMI2/bomi2_phenotyped_densities_sep.xlsx", sheet = "APC")
PhenoAll <- as.data.frame(PhenoAll)
PhenoAllNoCK <- as.data.frame(PhenoAllNoCK)
PhenoTIL <- as.data.frame(PhenoTIL)
PhenoNK <- as.data.frame(PhenoNK)
PhenoAPC <- as.data.frame(PhenoAPC)
PhenoAll$Location <- factor(PhenoAll$Location, levels = c("Tumor", "Stroma", "SAndT"))
PhenoAllNoCK$Location <- factor(PhenoAllNoCK$Location, levels = c("Tumor", "Stroma", "SAndT"))
PhenoTIL$Location <- factor(PhenoTIL$Location, levels = c("Tumor", "Stroma", "SAndT"))
PhenoNK$Location <- factor(PhenoNK$Location, levels = c("Tumor", "Stroma", "SAndT"))
PhenoAPC$Location <- factor(PhenoAPC$Location, levels = c("Tumor", "Stroma", "SAndT"))
PhenoAll$PhenoType <- factor(PhenoAll$PhenoType, levels = c("CD4_Eff","CD4_Treg","CD8_Eff","CD8_Treg","B_cells","NK_cells","NKT_cells","M1","M2","CD163","iDC","mDC","pDC","PanCKsingle_TIL", "PanCKsingle_NK","PanCKsingle_APC"))
PhenoAllNoCK$PhenoType <- factor(PhenoAllNoCK$PhenoType, levels = c("CD4_Eff","CD4_Treg","CD8_Eff","CD8_Treg","B_cells","NK_cells","NKT_cells","M1","M2","CD163","iDC","mDC","pDC"))
PhenoTIL$PhenoType <- factor(PhenoTIL$PhenoType, levels = c("CD4_Eff","CD4_Treg","CD8_Eff","CD8_Treg","B_cells","PanCKsingle_TIL"))
PhenoNK$PhenoType <- factor(PhenoNK$PhenoType, levels = c("NK_cells","NKT_cells","M1","M2","CD163","PanCKsingle_NK"))
PhenoAPC$PhenoType <- factor(PhenoAPC$PhenoType, levels = c("iDC","mDC","pDC","PanCKsingle_APC"))
fig12a <- ggplot(PhenoAllNoCK, aes(x = PhenoType, y = value, fill=giniGroup)) +
geom_boxplot(outlier.shape = 16, outlier.size = 2, ) +
theme_classic() +
# coord_flip() +
# scale_fill_npg() +
theme(legend.position = "none") +
stat_compare_means(paired = NULL, size=2) +
facet_wrap(~Location, nrow = 3, strip.position = "left", scale="free") +
scale_y_continuous(expand = c(0,0))
fig12b <- ggplot(PhenoAllNoCK, aes(x = Location, y = value, fill=giniGroup)) +
geom_boxplot(outlier.shape = 16, outlier.size = 2, ) +
theme_classic() +
# coord_flip() +
# scale_fill_npg() +
theme(legend.position = "none") +
stat_compare_means(paired = NULL, size=1.5) +
facet_wrap(~PhenoType, nrow = 3, strip.position = "left", scale="free")
Fig13 Distance data
DistTIL <- read_excel("~/Project/TCR/Distance/BOMI2_dist.xlsx", sheet = "TIL_Gini")
DistTIL <- as.data.frame(DistTIL)
TIL_corrT <- rcorr(as.matrix(DistTIL[,c(5,27:56)]), type = "spearman")
write.csv(TIL_corrT$r, file = "~/Project/TCR/Distance/TIL_corr.csv")
write.csv(TIL_corrT$P, file = "~/Project/TCR/Distance/TIL_corr_Pvalue.csv")
# melt
TIL_corr_melt <- read_excel("~/Project/TCR/Distance/BOMI2_dist.xlsx", sheet = "TIL_Corr")
TIL_corr_melt[,"P_adjust"] <- p.adjust(TIL_corr_melt$`P-Value`,method = "BH")
TIL_corr_melt$label <- NULL
TIL_corr_melt.1d[TIL_corr_melt.1d$var1]
TIL_corr_melt[TIL_corr_melt$P_adjust <= 0.001,"label"] <- "***"
TIL_corr_melt[TIL_corr_melt$P_adjust > 0.001 & TIL_corr_melt$P_adjust <= 0.01,"label"] <- "**"
TIL_corr_melt[TIL_corr_melt$P_adjust > 0.01 & TIL_corr_melt$P_adjust <= 0.05,"label"] <- "*"
TIL_corr_melt$Correlationggplot(TIL_corr_melt, aes(var1, var2, fill = Correlation)) +
geom_point(shape=21, colour = "black", size = 12)+
theme_classic()+
scale_fill_gradientn(colours = rev(brewer.pal(5, "Spectral")))+
labs(title = "Correlation", x = "Outset", y = "End") +
theme(axis.text = element_text(size = 10, colour = "black"), axis.title = element_text(size = 10),plot.title = element_text(size=15))+
geom_text(aes(label = label), size = 5, colour = "black")1D plot
TIL_corr_melt.1d <- read_excel("~/Project/TCR_Summary/Writing/SumSup.xlsx", sheet = "TIL_Corr_1D")
TIL_corr_melt.1d <- as.data.frame(TIL_corr_melt.1d)
# -0.122419365 0.138273993
#TIL_corr_melt.1d[TIL_corr_melt.1d$var1 == "CD4_Treg" & TIL_corr_melt.1d$var2 == "PanCKsingle", c("Correlation","P-Value")] <- c(-0.122419365, 0.138273993)
TIL_corr_melt.1d[,"P_adjust"] <- p.adjust(TIL_corr_melt.1d$`P-Value`,method = "BH")
TIL_corr_melt.1d[TIL_corr_melt.1d$P_adjust <= 0.001,"label"] <- "***"
TIL_corr_melt.1d[TIL_corr_melt.1d$P_adjust > 0.001 & TIL_corr_melt.1d$P_adjust <= 0.01,"label"] <- "**"
TIL_corr_melt.1d[TIL_corr_melt.1d$P_adjust > 0.01 & TIL_corr_melt.1d$P_adjust <= 0.05,"label"] <- "*"ggplot(TIL_corr_melt.1d, aes(var2, var1, fill = Correlation)) +
geom_point(shape=21, colour = "black", size = 12)+
theme_classic()+
scale_fill_gradientn(colours = rev(brewer.pal(5, "Spectral")))+
labs(title = "Correlation", x = "Outset", y = "End") +
theme(axis.text = element_text(size = 10, colour = "black"), axis.title = element_text(size = 10),plot.title = element_text(size=15))+
geom_text(aes(label = label), size = 5, colour = "black")
Fig14 immunetherapy treated cohort
library(readxl)
itCohort <- read_excel("~/Project/TCR/ImmuneCohort/GSE126044_gini_ab.xlsx",
sheet = "Sheet3")
View(itCohort)
itCohort <- as.data.frame(itCohort)
print(fig14b_test)
Call: survfit(formula = Surv(`Progression free survival (month)`, Status) ~
giniGroup, data = itCohort)
n events median 0.95LCL 0.95UCL
giniGroup=High 5 3 13.83 13.5 NA
giniGroup=Low 4 4 2.87 1.1 NAsummary(fig14b_test)$table records n.max n.start events rmean se(rmean) median 0.95LCL 0.95UCL
giniGroup=High 5 5 5 3 14.02667 1.7040449 13.833333 13.5 NA
giniGroup=Low 4 4 4 4 2.60000 0.4980518 2.866667 1.1 NA
surv_median(fig14b_test)Warning: `select_()` was deprecated in dplyr 0.7.0.
Please use `select()` instead.Figure 15 Lymph nodes
Beta clone distance
GiniDist <- read.table("/Users/huiyu818/Project/TCR/TCRdist//cloneSummary/beta_tumor_dist_rank.txt")
GiniDist <- as.data.frame(GiniDist)pheatmap(GiniDist,
cluster_rows = F, cluster_cols = F,
#cutree_cols = 3,
color = colorRampPalette(brewer.pal(n = 7, name = "RdYlBu"))(100),
fontsize = 5,
show_colnames = T
)# Load the annotation
GiniDist_anno <- read_excel("~/Project/TCR/TCRdist/cloneSummary/beta_tumor.xlsx", sheet = "Distance_DF_2")
GiniDist_anno <- as.data.frame(GiniDist_anno)
rownames(GiniDist_anno) <- GiniDist_anno$clone_id_newGiniDist_anno_color = list(
# "GiniIndex" = colorRampPalette(brewer.pal(n = 9, name = "Reds"))(100),
"Anti_virus" = c(Y = "#D45F51", N = "#B7D2E8"))
pheatmap(GiniDist,
clustering_method = "ward.D2",
clustering_distance_rows = "euclidean", clustering_distance_cols = "euclidean",
color = colorRampPalette(brewer.pal(n = 7, name = "RdYlBu"))(100),
annotation_col = GiniDist_anno["Anti_virus"],
annotation_colors = GiniDist_anno_color,
fontsize = 5,
show_colnames = T
)pheatmap(t(ihcHeatT_tumor_norm2), clustering_method = "ward.D2",
clustering_distance_rows = "euclidean", clustering_distance_cols = "euclidean",
cutree_cols = 3,
#color = colorRampPalette(brewer.pal(n = 7, name = "YlGnBu"))(100),
annotation_col = ihcAnno,
annotation_colors = ihcAnno_color,
fontsize = 5,
show_colnames = F
)Cancer testis antigens
CTA_list <- read_excel("~/Project/TCR_Summary/Writing/SumSup.xlsx", sheet = "CTA")
CTA_list <- as.data.frame(CTA_list)Bomi2RC_tumor_norm <- read.csv("~/Project/Clinical/BOMI2/BOMI2_RNAseq_FPKM/RNASeq_counts_tumor_norm.txt", sep="\t")CTA list 1 (all from database)
setnames(GiniClin_CTA_ori, old = CTA_list[["Ensgid_1"]], new = CTA_list[["Gene_1"]])
Error in setnames(GiniClin_CTA_ori, old = CTA_list[["Ensgid_1"]], new = CTA_list[["Gene_1"]]) :
Items of 'old' not found in column names: [ENSG00000183461, ENSG00000204379, ENSG00000204382, ENSG00000204376, ENSG00000204375, ENSG00000221867, ENSG00000197172, ENSG00000137948, ENSG00000123584, ENSG00000183305, ...]. Consider skip_absent=TRUE.GiniClin_CTA_ori[1,37]
[1] 2.098581CTA_corr <- rcorr(as.matrix(GiniClin_CTA_ori[,c(5,37:132)]), type = "spearman")
write.csv(CTA_corr$r, file = "~/Project/TCR/CTA/ctaCor.csv")
write.csv(CTA_corr$P, file = "~/Project/TCR/CTA/ctaPvalue.csv")CTA_corrT <- rename(CTA_corrT, c("...1"="GeneSymbol"))
Error in `rename()`:
! Can't rename columns that don't exist.
✖ Column `GeneSymbol` doesn't exist.
Backtrace:
1. dplyr::rename(CTA_corrT, c(...1 = "GeneSymbol"))
2. dplyr:::rename.data.frame(CTA_corrT, c(...1 = "GeneSymbol"))CTA list 2 (noval list)
GiniClin_CTA_ori_2 <- merge(GiniClin_tumor, GiniClin_CTA_ori_2, by.x = "ID" ,by.y = "row.names")
setnames(GiniClin_CTA_ori_2, old = CTA_list[["Ensgid_2"]], new = CTA_list[["Gene_2"]])
Error in setnames(GiniClin_CTA_ori_2, old = CTA_list[["Ensgid_2"]], new = CTA_list[["Gene_2"]]) :
NA in 'new' at positions [91, 92, 93, 94, 95, 96]GiniClin_CTA_ori_2[3,37]
[1] 0.9198992CTA_corr_2 <- rcorr(as.matrix(GiniClin_CTA_ori_2[,c(5,37:126)]), type = "spearman")
write.csv(CTA_corr_2$r, file = "~/Project/TCR/CTA/ctaCor_list2.csv")
write.csv(CTA_corr_2$P, file = "~/Project/TCR/CTA/ctaPvalue_list2.csv")
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Error: attempt to use zero-length variable name
<!-- rnb-output-end -->
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```r
CTA_corrT_2 <- read_excel("~/Project/TCR/CTA/CTA.xlsx", sheet = "GeneList2")
CTA_corrT_2 <- as.data.frame(CTA_corrT_2)
CTA_corrT_2[,"padj"] <- p.adjust(CTA_corrT_2$Pvalue,method = "BH")Vocanol plot
ggplot(data = diffGene, aes(x = log2FoldChange, y = -log10(padj), color = sign)) +
geom_point(size = 1) + #Create scatter plot
scale_color_manual(values = c('red', 'gray', 'green'), limits = c('UP', 'none', 'DOWN')) + #Set the color
labs(x = 'log2 Fold Change', y = '-log10 adjust p-value', title = 'High clonality vs Low clonality', color = '') + #Set the labels
theme(plot.title = element_text(hjust = 0.5, size = 14), panel.grid = element_blank(), #Change the them and grid lines
panel.background = element_rect(color = 'black', fill = 'transparent'),
legend.key = element_rect(fill = 'transparent')) +
geom_vline(xintercept = c(-1, 1), lty = 3, color = 'black') + #Add threshold line
geom_hline(yintercept = 2, lty = 3, color = 'black') +
xlim(-12, 12) + ylim(0, 35) + #Set the curve boundary
geom_text_repel(
data = diffGene,
aes(label = label),
size = 3, segment.color = "blue", show.legned = FALSE,
point.padding = unit(0.8, "lines")
)ISS plot
library(dplyr)
ISS_dot_data <- ISS_dot_data %>%
group_by(Sample) %>%
mutate(Normalized_Clone_fraction = scale(Clone_fraction), # Normalizing clone fraction
Normalized_Clone_mean = scale(Clone_mean)) %>% # Normalizing clone mean
ungroup()ggplot(ISS_dot_data, aes(Clone, EPCAM_CDH1_anno)) +
geom_point(aes(size=Normalized_Clone_fraction, color = Normalized_Clone_mean))+
facet_wrap(~ Sample, scales = "free_x", drop = TRUE) +
theme_bw()+
theme(panel.grid.major = element_blank(), # Remove major grid lines
panel.grid.minor = element_blank()) + # Remove minor grid lines
scale_size(range = c(1, 10)) +
scale_color_gradientn(colors = rev(brewer.pal(5, "Spectral")))+
labs(title = "Bubble plot grouped by sample", x = "Clone", y = "EPCAM_CDH1 annotation") +
theme(axis.text.x = element_text(size = 10, colour = "black", angle = 45), axis.title = element_text(size = 10),plot.title = element_text(size=15))
ggplot(ISS_dot_data_L766, aes(Clone, EPCAM_CDH1_anno)) +
geom_point(aes(size=Clone_fraction, color = Clone_mean))+
theme_bw()+
theme(panel.grid.major = element_blank(), # Remove major grid lines
panel.grid.minor = element_blank()) + # Remove minor grid lines
scale_color_gradientn(colors = rev(brewer.pal(5, "Spectral")))+
labs(title = "Bubble plot grouped by sample", x = "Clone", y = "EPCAM_CDH1 annotation") +
theme(axis.text.x = element_text(size = 10, colour = "black", angle = 45), axis.title = element_text(size = 10),plot.title = element_text(size=15))
---
title: "R Notebook"
output: html_notebook
---

```{r}
library(readxl)
library(clusterProfiler)

library(GOplot)
library(DOSE)

library(topGO)
library(circlize)
library("pheatmap")

library("ggsci")
library("ggplot2")
library("gridExtra")

library("survival")
library("survminer")

library(ggpubr)
library(rstpm2)
library(corrplot)
library(Hmisc)

library(cowplot)
library(reshape)

library(ggbreak)
library(ggrepel)
```


```{r}
GiniClin <- read_excel("~/Project/TCR_Summary/Writing/SumSup.xlsx", sheet = "Gini_Clinic")

GiniClin <- as.data.frame(GiniClin, row.names = GiniClin$ID)

```

# Figure 1

```{r}
f1a <- ggplot(GiniClin, aes(x=GiniIndex)) +
  geom_histogram(colour = "black", fill="#4dbbd5ff", bins = 50) +
  theme_classic() +
  theme(axis.text = element_text(size = 20)) +
  labs(title = "Gini_distribution", y = "Patient count") +
  scale_x_continuous(expand = c(0,0), limits = c(0, 0.8)) + scale_y_continuous(expand = c(0,0), limits = c(0,25))

f1a
```

```{r}
qqnorm(GiniClin_tumor$GiniIndex, ylab = 'GiniIndex')
qqline(GiniClin_tumor$GiniIndex) # 增加趋势直线，利于比较
```

```{r}
# H0 is that the data fit normal distribution
shapiro.test(GiniClin_tumor$GiniIndex)
```



```{r}
GiniClin_AcSq <- GiniClin[GiniClin$histology_Hans_B_WHO_2015 %in% c("AC","SqCC"), ]

f1a_2 <- ggplot(GiniClin_AcSq, aes(x=GiniIndex, fill = histology_Hans_B_WHO_2015)) +
  geom_histogram(color = "black", position = "identity", bins = 50, size = 0.3) +
  theme_classic() +
  theme(axis.text = element_text(size = 20, colour = "black"), legend.position = c(0.8,0.8)) +
  labs(title = "Gini_distribution", fill = "Histology") +
  scale_fill_manual(values = alpha(c("#F8766D", "#00BFC4"), 0.75)) +
  scale_x_continuous(expand = c(0,0), limits = c(0,0.85)) + scale_y_continuous(expand = c(0,0), limits = c(0,17), breaks = c(seq(0,15,5)))

f1a_2
```


```{r}
GiniClin_tumor <- read_excel("~/Project/TCR_Summary/Writing/SumSup.xlsx", sheet = "Gini_Clinic_tumor")

GiniClin_tumor <- as.data.frame(GiniClin_tumor, row.names = GiniClin_tumor$ID)
```




## Box plot
```{r}
# Age group
GiniClin_tumor[GiniClin_tumor$age >= 70 ,"ageGroup"] <- "Above 70"
GiniClin_tumor[GiniClin_tumor$age < 70 ,"ageGroup"] <- "Under 70"

# f1b_1 <- ggplot(GiniClin_tumor, aes(x = ageGroup, y = GiniIndex, fill=ageGroup)) +

f1b_1 <- ggplot(GiniClin_tumor, aes(x = ageGroup, y = GiniIndex, fill=ageGroup)) +
  geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black", notch = FALSE) +
  theme_classic() +
  #coord_flip() +
  #scale_color_npg() +
  labs(title = "Age", x = "Age") +
  theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
  stat_compare_means(paired = NULL)

f1b_1
```

```{r}
GiniClin_tumor[GiniClin_tumor$`stadium numbers edt 8 (PMI)` >= 5 ,"stageGroup"] <- "High stage"
GiniClin_tumor[GiniClin_tumor$`stadium numbers edt 8 (PMI)` < 5 ,"stageGroup"] <- "Low stage"

f1b_2 <- ggplot(GiniClin_tumor, aes(x = stageGroup, y = GiniIndex, fill=stageGroup)) +
  geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
  theme_classic() +
  # coord_flip() +
  # scale_color_npg() +
  labs(title = "Stage", x = "Stage") +
  theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
  stat_compare_means(paired = NULL)

f1b_2
```


```{r}
# histology
f1b_3 <- ggplot(GiniClin_tumor, aes(x = histology_Hans_B_WHO_2015, y = GiniIndex, fill=histology_Hans_B_WHO_2015)) +
  geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
  theme_classic() +
  #coord_flip() +
  scale_fill_manual(values = c("#F8766D", "#999999", "#00BF7D","#00B0F6","#A3A500", "#00BFC4")) +
  labs(title = "Histology", x = "Histology") +
  theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
  stat_compare_means(paired = NULL)

f1b_3
```

```{r}
GiniClin_tumor[GiniClin_tumor$gender == "M" ,"gender"] <- "Male"
GiniClin_tumor[GiniClin_tumor$gender == "F" ,"gender"] <- "Female"

f1b_4 <- ggplot(GiniClin_tumor, aes(x = gender, y = GiniIndex, fill=gender)) +
  geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
  theme_classic() +
  # coord_flip() +
  # scale_fill_npg() +
  labs(title = "Gender", x = "Sex") +
  theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
  stat_compare_means(paired = NULL)

f1b_4

```

```{r}
# Smoke
GiniClin_tumor[GiniClin_tumor$smoke == "N" ,"smoke"] <- "Never smoke"
GiniClin_tumor[GiniClin_tumor$smoke == "C" ,"smoke"] <- "Current smoke"
GiniClin_tumor[GiniClin_tumor$smoke == "F" ,"smoke"] <- "Former smoke"

f1b_5 <- ggplot(GiniClin_tumor, aes(x = smoke, y = GiniIndex, fill=smoke)) +
  geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
  theme_classic() +
  # coord_flip() +
  scale_fill_manual(values = c("#F8766D", "#00BFC4", "#999999")) +
  labs(title = "Smoke", x = "Smoke") +
  theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
  stat_compare_means(paired = NULL)

f1b_5
```

```{r}
# Smoke
GiniClin_tumor[GiniClin_tumor$smoke == "Never smoke" ,"smoke_2"] <- "Non smoker"
GiniClin_tumor[GiniClin_tumor$smoke == "Current smoke" | GiniClin_tumor$smoke == "Former smoke","smoke_2"] <- " Smoker"

f1b_5_2 <- ggplot(GiniClin_tumor, aes(x = smoke_2, y = GiniIndex, fill=smoke_2)) +
  geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
  theme_classic() +
  # coord_flip() +
  scale_fill_manual(values = c("#F8766D", "#00BFC4")) +
  labs(title = "Smoke", x = "Smoke") +
  theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
  stat_compare_means(paired = FALSE)

f1b_5_2
```

```{r}
# WHO PS
GiniClin_tumor[GiniClin_tumor$WHO_PS == 0 ,"WHO_PS_group"] <- 0
GiniClin_tumor[GiniClin_tumor$WHO_PS > 0 ,"WHO_PS_group"] <- "> 0"

f1b_6 <- ggplot(GiniClin_tumor, aes(x = WHO_PS_group, y = GiniIndex, fill=WHO_PS_group)) +
  geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
  theme_classic() +
  # coord_flip() +
  scale_fill_manual(values = c("#F8766D", "#00BFC4")) +
  labs(title = "WHO performance status", x = "WHO performance rank") +
  theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
  stat_compare_means(paired = FALSE)

f1b_6
```

### AC/SqCC
```{r}
ggplot(GiniClin_tumor[GiniClin_tumor$histology_Hans_B_WHO_2015 == "AC", ], aes(x = stageGroup, y = GiniIndex, fill=stageGroup)) +
  geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
  theme_classic() +
  # coord_flip() +
  # scale_color_npg() +
  labs(title = "Stage", x = "Stage") +
  theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
  stat_compare_means(paired = NULL)
```

```{r}
ggplot(GiniClin_tumor[GiniClin_tumor$histology_Hans_B_WHO_2015 == "SqCC", ], aes(x = stageGroup, y = GiniIndex, fill=stageGroup)) +
  geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
  theme_classic() +
  # coord_flip() +
  # scale_color_npg() +
  labs(title = "Stage", x = "Stage") +
  theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
  stat_compare_means(paired = NULL)
```

```{r}
ggplot(GiniClin_tumor[GiniClin_tumor$histology_Hans_B_WHO_2015 == "AC", ], aes(x = WHO_PS_group, y = GiniIndex, fill=WHO_PS_group)) +
  geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
  theme_classic() +
  # coord_flip() +
  scale_fill_manual(values = c("#F8766D", "#00BFC4")) +
  labs(title = "WHO performance status", x = "WHO performance rank") +
  theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
  stat_compare_means(paired = FALSE)
```

```{r}
ggplot(GiniClin_tumor[GiniClin_tumor$histology_Hans_B_WHO_2015 == "SqCC", ], aes(x = WHO_PS_group, y = GiniIndex, fill=WHO_PS_group)) +
  geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
  theme_classic() +
  # coord_flip() +
  scale_fill_manual(values = c("#F8766D", "#00BFC4")) +
  labs(title = "WHO performance status", x = "WHO performance rank") +
  theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
  stat_compare_means(paired = FALSE)
```




# Figure 2
```{r}
library("survival")
library("survminer")
```

## Figure2A: KM plot

### All patient


```{r}
GiniClin_tumor[GiniClin_tumor$GiniIndex >= 0.25431, "giniGroup"] <- "Above median"
GiniClin_tumor[GiniClin_tumor$GiniIndex < 0.25431, "giniGroup"] <- "Under median"
GiniClin_tumor$giniGroup = factor(GiniClin_tumor$giniGroup, levels = c("Above median", "Under median"))
GiniClin_tumor['L481T', 'status_5_years_trunk_20190329'] = 1
GiniClin_tumor2 <- GiniClin_tumor[!GiniClin_tumor$ID == 'L584T',]
```

```{r}
# construct the object
fig2test <- survfit(Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~ giniGroup, data = GiniClin_tumor)

print(fig2test)

summary(fig2test)$table

```

```{r}
fig2a_all <- ggsurvplot(fig2test,
                        pval = TRUE,
                        palette = c("#e64b35", "#4dbbd5"),
                        break.time.by = 1,
                        ggtheme = theme_classic(),
                        legend.labs = c("Above median", "Under median"),
                        risk.table = "abs_pct",
                        risk.table.y.text = FALSE,
                        xlab = "Time (years)",
                        title = "giniSurv",
                        xlim = c(0,5.2),
                        axes.offset = FALSE,
                        risk.table.fontsize = 2.5,
                        legend = c(0.9,0.9),
                        risk.table.title = "Number at risk by time: n (%)",
                        font.main = c())


fig2a_all
```

```{r}
fig2a_continue.cox <- coxph(Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~ GiniIndex, data = GiniClin_tumor)
fig2a_continue.cox
```

```{r}
ggsurvplot(survfit(fig2a_continue.cox, data=GiniClin_tumor), color = "#2E9FDF",ggtheme = theme_minimal())
```


#### Time split surv
```{r}
GiniClin_tumor[GiniClin_tumor$giniGroup == "Above median", "giniGroup_nr"] <- 1
GiniClin_tumor[GiniClin_tumor$giniGroup == "Under median", "giniGroup_nr"] <- 0

fig2_timespl <- survSplit(Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329 == 1) ~ ., data=GiniClin_tumor2, cut=c(2), episode = "tgroup")

fig2_timespl.cox <- coxph(Surv(tstart, OS_years_5_years_trunk_20190329, event==1) ~ giniGroup_nr:strata(tgroup), data=fig2_timespl, ties="efron")
summary(fig2_timespl.cox)

```

```{r}
cox.zph(fig2_timespl.cox)
```

```{r}
fig2_mod_tvc <- stpm2(Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329 == 1) ~ giniGroup_nr, data = GiniClin_tumor, df=3, tvc=list(giniGroup_nr=1))

plot(fig2_mod_tvc, newdata = data.frame(giniGroup_nr = 0), type = "hazard",  xlim=c(0.5,5), ylim=c(0,0.2), ylab = "Hazard", xlab = "Time", lwd = 2, ci = FALSE)
plot(fig2_mod_tvc, newdata = data.frame(giniGroup_nr = 1), type = "hazard", line.col=2, ci=FALSE, lwd = 2, add=TRUE)
```

#### Calculate best cutoff
```{r}
fig2.cut <- surv_cutpoint(GiniClin_tumor,
                          time = "OS_years_5_years_trunk_20190329",
                          event = "status_5_years_trunk_20190329",
                          variables = "GiniIndex")
summary(fig2.cut)
```
```{r}
plot(fig2.cut, "GiniIndex", palette = "npg")
```
```{r}
fig2.cut.cat <- surv_categorize(fig2.cut)
fig2.cut.fit <- survfit(Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~ GiniIndex, data = fig2.cut.cat)

ggsurvplot(fig2.cut.fit,
           data = fig2.cut.cat,
           risk.table = TRUE,
           pval = T)
```


#### Calculate AC and SqCC

```{r}
# AC
fig2.cut_AC <- surv_cutpoint(GiniClin_tumor_Ac,
                             time = "OS_years_5_years_trunk_20190329",
                             event = "status_5_years_trunk_20190329",
                             variables = "GiniIndex")
summary(fig2.cut_AC)
```
```{r}
plot(fig2.cut_AC, "GiniIndex", palette = "npg")
```
```{r}
fig2.cut_AC.cat <- surv_categorize(fig2.cut_AC)
fig2.cut_AC.fit <- survfit(Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~ GiniIndex, data = fig2.cut_AC.cat)

ggsurvplot(fig2.cut_AC.fit,
           data = fig2.cut.cat,
           risk.table = TRUE,
           pval = T)
```


#### KM without censoring
```{r}
fig2_NoCensor_test <- survfit(Surv(OS_years_20190329, status_out_20190329) ~ giniGroup, data = GiniClin_tumor)

print(fig2_NoCensor_test)

summary(fig2_NoCensor_test)$table
```



```{r}
fig2_NoCensor <- ggsurvplot(fig2_NoCensor_test,
                        pval = TRUE,
                        palette = c("#e64b35", "#4dbbd5"),
                        break.time.by = 2,
                        ggtheme = theme_classic(),
                        legend.labs = c("Above median", "Under median"),
                        risk.table = "abs_pct",
                        risk.table.y.text = FALSE,
                        xlab = "Time (years)",
                        title = "giniSurv_without_censoring",
                        axes.offset = FALSE,
                        risk.table.fontsize = 2.5,
                        legend = c(0.9,0.9),
                        risk.table.title = "Number at risk by time: n (%)",
                        font.main = c())

fig2_NoCensor
```


```{r}
fig2_NoCensor.cut <- surv_cutpoint(GiniClin_tumor,
                          time = "OS_years_20190329",
                          event = "status_out_20190329",
                          variables = "GiniIndex")
summary(fig2_NoCensor.cut)
```

```{r}
plot(fig2_NoCensor.cut, "GiniIndex", palette = "npg")
```
```{r}
fig2_NoCensor.cut.cat <- surv_categorize(fig2_NoCensor.cut)
fig2_NoCensor.cut.fit <- survfit(Surv(OS_years_20190329, status_out_20190329) ~ GiniIndex, data = fig2_NoCensor.cut.cat)

ggsurvplot(fig2_NoCensor.cut.fit,
           data = fig2_NoCensor.cut.cat,
           risk.table = TRUE,
           pval = T)
```

```{r}
ggsurvplot(fig2_NoCensor.cut.fit,
            pval = TRUE,
            palette = c("#e64b35", "#4dbbd5"),
            break.time.by = 2,
            ggtheme = theme_classic(),
            legend.labs = c("Above median", "Under median"),
            risk.table = "abs_pct",
            risk.table.y.text = FALSE,
            xlab = "Time (years)",
            title = "giniSurv_without_censoring",
            axes.offset = FALSE,
            risk.table.fontsize = 2.5,
            legend = c(0.9,0.9),
            risk.table.title = "Number at risk by time: n (%)",
            font.main = c())
```




### AC patient

```{r}
GiniClin_tumor_Ac = GiniClin_tumor[GiniClin_tumor$histology_Hans_B_WHO_2015 == "AC", ]

fig2ACtest <- survfit(Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~ giniGroup, data = GiniClin_tumor_Ac)

print(fig2ACtest)
print("====")
summary(fig2ACtest)$table
```

```{r}
fig2a_AC <- ggsurvplot(fig2ACtest,
                        pval = TRUE,
                        palette = c("#e64b35", "#4dbbd5"),
                        break.time.by = 1,
                        ggtheme = theme_classic(),
                        #legend.labs = c("Above median", "Under median"),
                        risk.table = "abs_pct",
                        risk.table.y.text = FALSE,
                        xlab = "Time (years)",
                        xlim = c(0,5.2),
                        axes.offset = FALSE,
                        risk.table.fontsize = 2.5,
                        legend = c(0.9,0.9),
                        risk.table.title = "Number at risk by time: n (%)",
                        font.main = c())


fig2a_AC
```

#### without sensoring
```{r}
fig2AC_NoCensor_test <- survfit(Surv(OS_years_20190329, status_out_20190329) ~ giniGroup, data = GiniClin_tumor[GiniClin_tumor$histology_Hans_B_WHO_2015 == "AC", ])

print(fig2AC_NoCensor_test)

summary(fig2AC_NoCensor_test)$table
```

```{r}
ggsurvplot(fig2AC_NoCensor_test,
          pval = TRUE,
          palette = c("#e64b35", "#4dbbd5"),
          break.time.by = 1,
          ggtheme = theme_classic(),
          legend.labs = c("Above median", "Under median"),
          risk.table = "abs_pct",
          risk.table.y.text = FALSE,
          xlab = "Time (years)",
          title = "giniSurv_without_censoring",
          axes.offset = FALSE,
          risk.table.fontsize = 2.5,
          legend = c(0.9,0.9),
          risk.table.title = "Number at risk by time: n (%)",
          font.main = c())
```


### SqCC patient

```{r}
GiniClin_tumor_SqCC = GiniClin_tumor[GiniClin_tumor$histology_Hans_B_WHO_2015 == "SqCC", ]

fig2SqCCtest_2 <- survfit(Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~ giniGroup, data = GiniClin_tumor_SqCC)

print(fig2SqCCtest)
print("====")
summary(fig2SqCCtest)$table
```

```{r}
fig2a_SqCC_2 <- ggsurvplot(fig2SqCCtest_2,
                        pval = TRUE,
                        palette = c("#e64b35", "#4dbbd5"),
                        break.time.by = 1,
                        ggtheme = theme_classic(),
                        legend.labs = c("Above median", "Under median"),
                        risk.table = "abs_pct",
                        risk.table.y.text = FALSE,
                        xlab = "Time (years)",
                        xlim = c(0,5.2),
                        axes.offset = FALSE,
                        risk.table.fontsize = 2.5,
                        legend = c(0.9,0.9),
                        risk.table.title = "Number at risk by time: n (%)",
                        font.main = c())


fig2a_SqCC_2
```





```{r}
fig2_SqCC_timespl <- survSplit(Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329 == 1) ~ ., data=GiniClin_tumor_SqCC, cut=2, episode = "tgroup")

fig2_SqCC_timespl.cox <- coxph(Surv(tstart, OS_years_5_years_trunk_20190329, event==1) ~ giniGroup:strata(tgroup), data=fig2_SqCC_timespl, ties="efron")
summary(fig2_SqCC_timespl.cox)
```

#### without sensoring
```{r}
fig2SqCC_NoCensor_test <- survfit(Surv(OS_years_20190329, status_out_20190329) ~ giniGroup, data = GiniClin_tumor[GiniClin_tumor$histology_Hans_B_WHO_2015 == "SqCC", ])

print(fig2SqCC_NoCensor_test)

summary(fig2SqCC_NoCensor_test)$table
```

```{r}
ggsurvplot(fig2SqCC_NoCensor_test,
          pval = TRUE,
          palette = c("#e64b35", "#4dbbd5"),
          break.time.by = 1,
          ggtheme = theme_classic(),
          legend.labs = c("Above median", "Under median"),
          risk.table = "abs_pct",
          risk.table.y.text = FALSE,
          xlab = "Time (years)",
          title = "giniSurv_without_censoring",
          axes.offset = FALSE,
          risk.table.fontsize = 2.5,
          legend = c(0.9,0.9),
          risk.table.title = "Number at risk by time: n (%)",
          font.main = c())
```


### Table: Cox regression

Single variate
```{r}
GiniClin_tumor$giniGroup = factor(GiniClin_tumor$giniGroup, levels = c("Under median", "Above median"))

fig2.cox <- coxph(Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~ giniGroup, data = GiniClin_tumor)

print("===")
fig2.cox
print("===")
print(summary(fig2.cox))
print("===")
```


```{r}
cox.zph(fig2.cox)
```


```{r}
# Merge the histology group except AC and SqCC to others
GiniClin_tumor[!(GiniClin_tumor$histology_Hans_B_WHO_2015 %in% c("AC","SqCC")), "Histology"] <- "Other"
GiniClin_tumor[GiniClin_tumor$histology_Hans_B_WHO_2015 == "AC", "Histology"] <- "AC"
GiniClin_tumor[GiniClin_tumor$histology_Hans_B_WHO_2015 == "SqCC", "Histology"] <- "SqCC"
```

```{r}
# Merge the current smoke and former smoke in to one group
GiniClin_tumor[GiniClin_tumor$smoke %in% c("Current smoke","Former smoke"), "smokeGroup"] <- "Current/Former"
GiniClin_tumor[GiniClin_tumor$smoke == "Never smoke", "smokeGroup"] <- "Never"
```


```{r}
# Construct levels of each parameter
GiniClin_tumor$stageGroup = factor(GiniClin_tumor$stageGroup, levels = c("Low stage", "High stage"))
GiniClin_tumor$gender = factor(GiniClin_tumor$gender, levels = c("Male", "Female"))
GiniClin_tumor$Histology = factor(GiniClin_tumor$Histology, levels = c("AC", "SqCC", "Other"))
GiniClin_tumor$ageGroup = factor(GiniClin_tumor$ageGroup, levels = c("Under 70", "Above 70"))
GiniClin_tumor$smokeGroup = factor(GiniClin_tumor$smokeGroup, levels = c("Never","Current/Former"))
```

#### Multi-variate cox regression

```{r}
fig2.multicox <- coxph(Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~ giniGroup + ageGroup + gender + smokeGroup + Histology + stageGroup, data = GiniClin_tumor)

summary(fig2.multicox)
```


### Without adjuvent treatment
```{r}
# Subset the group without adjuvent treatment
GiniClin_noTreat = subset(GiniClin_tumor, Annette_adjuvant == 0)

GiniClin_noTreat = as.data.frame(GiniClin_noTreat)

GiniClin_noTreat$giniGroup = factor(GiniClin_noTreat$giniGroup, levels = c("Above median", "Under median"))

# construct the object
fig8test <- survfit(Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~ giniGroup, data = GiniClin_noTreat)

print(fig8test)

summary(fig8test)$table
```


```{r}
fig8a_all <- ggsurvplot(fig8test,
                        pval = TRUE,
                        palette = c("#e64b35", "#4dbbd5"),
                        break.time.by = 1,
                        ggtheme = theme_classic(),
                        #legend.labs = c("Above median", "Under median"),
                        risk.table = "abs_pct",
                        risk.table.y.text = FALSE,
                        xlab = "Time (years)",
                        title = "giniSurv_no_treatment",
                        xlim = c(0,5.2),
                        axes.offset = FALSE,
                        risk.table.fontsize = 2.5,
                        legend = c(0.9,0.9),
                        risk.table.title = "Number at risk by time: n (%)",
                        font.main = c())


fig8a_all
```

```{r}
# Subset the group with adjuvent treatment
GiniClin_Treat = subset(GiniClin_tumor, Annette_adjuvant == 1)

GiniClin_Treat = as.data.frame(GiniClin_Treat)

GiniClin_Treat$giniGroup = factor(GiniClin_Treat$giniGroup, levels = c("Above median", "Under median"))

# construct the object
fig8test_2 <- survfit(Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~ giniGroup, data = GiniClin_Treat)

print(fig8test_2)

summary(fig8test_2)$table
```


```{r}
fig8b_all_2 <- ggsurvplot(fig8test_2,
                        pval = TRUE,
                        palette = c("#e64b35", "#4dbbd5"),
                        break.time.by = 1,
                        ggtheme = theme_classic(),
                        #legend.labs = c("Above median", "Under median"),
                        risk.table = "abs_pct",
                        risk.table.y.text = FALSE,
                        xlab = "Time (years)",
                        title = "giniSurv_treatment",
                        xlim = c(0,5.2),
                        axes.offset = FALSE,
                        risk.table.fontsize = 2.5,
                        legend = c(0.9,0.9),
                        risk.table.title = "Number at risk by time: n (%)",
                        font.main = c())


fig8b_all_2
```
# Figure mutation burden

## Mutation burden

```{r}
mutClin <- read_excel("~/Project/TCR_Summary/Writing/SumSup.xlsx", sheet = "MutationBurdern_all_tumor")

mutClin <- as.data.frame(mutClin, row.names = mutClin$ID)

mutClin$AB_GiniIndex <- as.numeric(mutClin$AB_GiniIndex)
mutClin$Mutation_load_allmutations <- as.numeric(mutClin$Mutation_load_allmutations)
mutClin <- mutClin[intersect(rownames(mutClin), rownames(GiniClin_tumor)),]
```

```{r}
fig4a <- ggscatter(mutClin[,c("AB_GiniIndex","Mutation_load_allmutations")], x = "AB_GiniIndex", y = "Mutation_load_allmutations",
                   add="reg.line", add.params = list(color = "#00bfc4"),
                   conf.int = FALSE,
                   cor.coef = TRUE,
                   cor.coeff.args = list(method = "spearman", label.x.npc = 0.15, label.y.npc = 0.85),
                   ggtheme = theme_classic(),
                   #add = "reg.line", add.params = c(color = '#00bfc4'),
                   title = "Mutation burden", xlab = "Gini Index", ylab = "Mutation load of all mutations",
                   font.tickslab = c(size=15, color = "black"), font.main = 20,font.y = 15, font.x = 15) +
  scale_y_continuous(expand = c(0,0), limits = c(0, 130), breaks = c(seq(0,120,30))) +
  scale_x_continuous(expand = c(0,0), limits = c(0, 0.75), breaks = c(seq(0,0.6,0.2)))

fig4a
```

## Mutation

```{r}
mutGini <- read_excel("~/Project/TCR_Summary/Writing/SumSup.xlsx", sheet = "mutation_tumor")

mutGini <- as.data.frame(mutGini, row.names = mutGini$ID)
mutGini$ID <- NULL

mutGini <- mutGini[intersect(rownames(mutGini), rownames(GiniClin_tumor)),]
mutGini$CCND1 <- NULL

mutTest <- list()
for (mutGene in colnames(mutGini[,(3:83)])){
  WT_buff <- mutGini[mutGini[,mutGene] == "WT","GiniIndex"]
  Mut_buff <- mutGini[mutGini[,mutGene] == "Mutated","GiniIndex"]
  mutRes <- wilcox.test(WT_buff,Mut_buff)
  mutTest <- c(mutTest,c(mutGene,mutRes$statistic,mutRes$p.value))
}

mutTest.matrix <- matrix(mutTest, ncol = 3, byrow = TRUE)
colnames(mutTest.matrix) <- c("Mutation", "wil.cox", "p_value")
mutTest.matrix <- as.data.frame(mutTest.matrix)
mutTest.matrix[,"FDR"] <- p.adjust(mutTest.matrix$p_value, method = "fdr")

```

### EGFR
```{r}
f4c.egfr <- ggplot(mutGini, aes(x = EGFR, y = GiniIndex, fill = EGFR)) +
  geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2,color ="black") +
  theme_classic() +
  # coord_flip() +
  scale_fill_manual(values = c("#FFE5D4", "#E6EBE0")) +
  labs(title = "EGFR", x = NULL) +
  theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
  stat_compare_means(paired = FALSE)
f4c.egfr
```

### APC
```{r}
f4c.apc <- ggplot(mutGini, aes(x = APC, y = GiniIndex, fill=APC)) +
  geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
  theme_classic() +
  # coord_flip() +
  scale_fill_manual(values = c("#FFE5D4", "#E6EBE0")) +
  labs(title = "APC", x = NULL) +
  theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
  stat_compare_means(paired = FALSE)
f4c.apc
```

### TP53
```{r}
f4c.tp53 <- ggplot(mutGini, aes(x = TP53, y = GiniIndex, fill=TP53)) +
  geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
  theme_classic() +
  # coord_flip() +
  scale_fill_manual(values = c("#FFE5D4", "#E6EBE0")) +
  labs(title = "TP53", x = NULL) +
  theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
  stat_compare_means(paired = FALSE)
f4c.tp53
```
### ARID1A
```{r}
f4c.arid1a <- ggplot(mutGini, aes(x = ARID1A, y = GiniIndex, fill=ARID1A)) +
  geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
  theme_classic() +
  # coord_flip() +
  scale_fill_manual(values = c("#FFE5D4", "#E6EBE0")) +
  labs(title = "ARID1A", x = NULL) +
  theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
  stat_compare_means(paired = FALSE)
f4c.arid1a
```

### EPHB6
```{r}
f4c.ephb6 <- ggplot(mutGini, aes(x = EPHB6, y = GiniIndex, fill=EPHB6)) +
  geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
  theme_classic() +
  # coord_flip() +
  scale_fill_manual(values = c("#FFE5D4", "#E6EBE0")) +
  labs(title = "EPHB6", x = NULL) +
  theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
  stat_compare_means(paired = FALSE)
f4c.ephb6
```

### CSMD3
```{r}
f4c.csmd3 <- ggplot(mutGini, aes(x = CSMD3, y = GiniIndex, fill=CSMD3)) +
  geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
  theme_classic() +
  # coord_flip() +
  scale_fill_manual(values = c("#FFE5D4", "#E6EBE0")) +
  labs(title = "CSMD3", x = NULL) +
  theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20, hjust = 0.5)) +
  #, panel.border = element_rect(color = "black", size = 1, fill = NA)
  stat_compare_means(paired = FALSE)
f4c.csmd3
```

### Combine
```{r}
ggarrange(f4c.egfr, f4c.apc, f4c.tp53, f4c.arid1a, f4c.ephb6, f4c.csmd3, ncol = 2, nrow = 3, align = "hv")
```



# Figure IHC Heatmap

## prepare table
```{r}
mut_t_anno <- read_excel("~/Project/TCR_Summary/Writing/SumSup.xlsx", sheet = "mutation_tumor_anno")

mut_t_anno <- as.data.frame(mut_t_anno, row.names = mut_t_anno$ID)

mut_t_anno$ID <- NULL

ihcAnno <- merge(AB_Gini_anno, mut_t_anno, by = "row.names", all =TRUE)
ihcAnno <- as.data.frame(ihcAnno, row.names = ihcAnno$Row.names)
ihcAnno$Row.names <- NULL
ihcAnno <- merge(ihcAnno, mutGini[,c("APC","ARID1A","EPHB6")],by = "row.names", all=FALSE)

ihcAnno <- merge(ihcAnno, GiniClin[,c("histology_Hans_B_WHO_2015", "gender", "smoke", "status_5_years_trunk_20190329")], by = "row.names",all =TRUE)

ihcAnno <- rename(ihcAnno, c("histology_Hans_B_WHO_2015"="histology", "status_5_years_trunk_20190329"="surv5years"))

ihcAnno <- as.data.frame(ihcAnno, row.names = ihcAnno$Row.names)
ihcAnno$Row.names <- NULL

ihcAnno <- ihcAnno[,c(1:20, 25:27, 21:24)]

ihcHeatT_tumor_norm <- as.data.frame(ihcHeatT_tumor_norm, row.names = ihcHeatT_tumor_norm$ID)
ihcHeatT_tumor_norm$ID <- NULL
ihcHeatT_tumor_norm <- as.data.frame(ihcHeatT_tumor_norm)

ihcHeatT_tumor_norm <- rename(ihcHeatT_tumor_norm, c("Gini Index" = "Gini.Index"))

ihcAnno[!is.na(ihcAnno$smoke) & ihcAnno$smoke == "N", "smoke"] <- "Never"
ihcAnno[!is.na(ihcAnno$smoke) & ihcAnno$smoke == "F", "smoke"] <- "Former"
ihcAnno[!is.na(ihcAnno$smoke) & ihcAnno$smoke == "C", "smoke"] <- "Current"

ihcAnno[!is.na(ihcAnno$gender) & ihcAnno$gender == "F", "gender"] <- "Female"
ihcAnno[!is.na(ihcAnno$gender) & ihcAnno$gender == "M", "gender"] <- "Male"

ihcAnno[!is.na(ihcAnno$surv5years) & ihcAnno$surv5years == 1, "surv5years"] <- "Dead"
ihcAnno[!is.na(ihcAnno$surv5years) & ihcAnno$surv5years == 0, "surv5years"] <- "Alive"

ihcHeatT_tumor_norm2 <- ihcHeatT_tumor_norm[!rownames(ihcHeatT_tumor_norm) %in% c("L483T", "L731T"), ]

ihcHeatT_tumor_norm3 <- ihcHeatT_tumor_norm2[,grep("CD44", colnames(ihcHeatT_tumor_norm2), invert = TRUE)]

```

```{r}
ihcAnno_color = list(
  "Gini index" = colorRampPalette(brewer.pal(n = 9, name = "Reds"))(100),
  "KRAS" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
  "EGFR" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
  "PIK3CA" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
  "FGFR2"= c(Mutated = "#D45F51", WT = "#B7D2E8"),
  "PDGFRA"= c(Mutated = "#D45F51", WT = "#B7D2E8"),
  "ROS1"= c(Mutated = "#D45F51", WT = "#B7D2E8"),
  "NF1" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
  "STK11" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
  "TP53" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
  "KEAP1" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
  "NFE2L2" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
  "MUC16" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
  "KMT2C" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
  "KMT2D" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
  "SMARCA4" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
  "CDKN2A" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
  "CREBBP" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
  "CSMD3" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
  "LRP1B" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
  "APC" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
  "ARID1A" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
  "EPHB6" = c(Mutated = "#D45F51", WT = "#B7D2E8"),
  "histology" = c(AC = "#D55740", SqCC = "#4DBCD6", AdSq = "#479E88", LCC = "#415384", LCNEC = "#F49B7F", SC = "#F8BED6"),
  "gender" = c(Male = "#B9E3DF", Female = "#EFC0D5"),
  "smoke" = c(Current = "#D45F51", Former = "#E68840", Never = "#BCD9B2"),
  "surv5years" = c(Dead = "#D45F51", Alive = "#B7D2E8")
)
```

```{r}
fig5b <- pheatmap(t(ihcHeatT_tumor_norm2), clustering_method = "ward.D2",
                  clustering_distance_rows = "euclidean", clustering_distance_cols = "euclidean",
                  cutree_cols = 3,
                  #color = colorRampPalette(brewer.pal(n = 7, name = "YlGnBu"))(100),
                  annotation_col = ihcAnno,
                  annotation_colors = ihcAnno_color,
                  fontsize = 5,
                  show_colnames = F
                  )
fig5b
```

```{r}
fig5b.cluster <- fig5b$tree_col

fig5b.cluster.plot <- plot(fig5b.cluster, hang = -1, cex=0.6, axes=FALSE, ann=FALSE)

fig5b.cut <- cutree(fig5b.cluster, 3)

fig5b.cut_1 <- names(fig5b.cut)[fig5b.cut == 1]
fig5b.cut_2 <- names(fig5b.cut)[fig5b.cut == 2]
fig5b.cut_3 <- names(fig5b.cut)[fig5b.cut == 3]

fig5b.cut_gini <- merge(AB_Gini_anno, fig5b.cut, by = "row.names", all.y = TRUE)

fig5b.cut_gini <- as.data.frame(fig5b.cut_gini, row.names = fig5b.cut_gini$Row.names)

fig5b.cut_gini$Row.names <- NULL

fig5b.cut_gini <- rename(fig5b.cut_gini, c("y"="Cluster"))
```

```{r}
fig5b.cut_gini[fig5b.cut_gini$Cluster == 2, "Class"] <- "Immune hot"
fig5b.cut_gini[fig5b.cut_gini$Cluster == 3, "Class"] <- "Median"
fig5b.cut_gini[fig5b.cut_gini$Cluster == 1, "Class"] <- "Immune cold"
fig5b.cut_gini$Class = factor(fig5b.cut_gini$Class, levels = c("Immune hot", "Median", "Immune cold"))
```


```{r}
fig5b.cut_gini <- as.data.frame(fig5b.cut_gini)
fig5b.cut_gini2 <- rename(fig5b.cut_gini, c("Gini index"="GiniIndex"))
compare_means(GiniIndex ~ Class, data = fig5b.cut_gini2)
```


```{r}
fig5b.cut_km <- merge(fig5b.cut_gini, GiniClin_tumor[, c("OS_years_5_years_trunk_20190329", "status_5_years_trunk_20190329")], by = "row.names")
fig5b.cut_km <- as.data.frame(fig5b.cut_km, row.names = fig5b.cut_km$Row.names)
fig5b.cut_km$Row.names <- NULL

fig5b.test <- survfit(Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~ Class, data = fig5b.cut_km)

summary(fig5b.test)$table
```

```{r}
fig5b.KM <- ggsurvplot(fig5b.test,
                        pval = TRUE,
                        palette = c("#e64b35", "#4dbbd5", "#999999"),
                        break.time.by = 1,
                        ggtheme = theme_classic(),
                        #legend.labs = c("Above median", "Under median"),
                        risk.table = "abs_pct",
                        risk.table.y.text = FALSE,
                        xlab = "Time (years)",
                        title = "giniSurv_Class",
                        xlim = c(0,5.2),
                        axes.offset = FALSE,
                        risk.table.fontsize = 2.5,
                        legend = c(0.9,0.9),
                        risk.table.title = "Number at risk by time: n (%)",
                        font.main = c())
fig5b.KM
```

```{r}
fig5c <- ggplot(fig5b.cut_gini, aes(x = Class, y = `Gini index`, fill=Class)) +
    geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
    theme_classic() +
    # coord_flip() +
    scale_fill_manual(values = c("#e64b35", "#4dbbd5", "#999999")) +
    labs(title = "Class", x = "Class") +
    theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
    stat_compare_means(paired = NULL)
fig5c
```


```{r}
fig5b.cut_clin <- merge(fig5b.cut_km, GiniClin_tumor[,c("histology_Hans_B_WHO_2015", "gender", "smoke")], by = "row.names", all.x = TRUE)

fig5b.cut_clin <- as.data.frame(fig5b.cut_clin, row.names = fig5b.cut_clin$Row.names)

fig5b.cut_clin$Row.names <- NULL

fig5b.cut_clin_AC <- fig5b.cut_clin[fig5b.cut_clin$histology_Hans_B_WHO_2015 == "AC", ]
fig5b.cut_clin_SqCC <- fig5b.cut_clin[fig5b.cut_clin$histology_Hans_B_WHO_2015 == "SqCC", ]

fig5b.cut_clin

fig5b.cut_clin[fig5b.cut_clin$`Gini index` >= 0.25431, "giniGroup"] <- "Above median"
fig5b.cut_clin[fig5b.cut_clin$`Gini index` < 0.25431, "giniGroup"] <- "Under median"
fig5b.cut_clin$giniGroup = factor(fig5b.cut_clin$giniGroup, levels = c("Above median", "Under median"))
```

## cluster without gini index

```{r}
pheatmap(t(ihcHeatT_tumor_norm2[,(2:25)]), clustering_method = "ward.D2",
                  clustering_distance_rows = "euclidean", clustering_distance_cols = "euclidean",
                  cutree_cols = 3,
                  #color = colorRampPalette(brewer.pal(n = 7, name = "YlGnBu"))(100),
                  annotation_col = ihcAnno,
                  annotation_colors = ihcAnno_color,
                  fontsize = 5,
                  show_colnames = F
                  )

pheatmap(t(ihcHeatT_tumor_norm3[,grep("_S", colnames(ihcHeatT_tumor_norm3))]), clustering_method = "ward.D2",
          clustering_distance_rows = "euclidean", clustering_distance_cols = "euclidean",
          cutree_cols = 3,
          #color = colorRampPalette(brewer.pal(n = 7, name = "YlGnBu"))(100),
          annotation_col = ihcAnno,
          annotation_colors = ihcAnno_color,
          fontsize = 5,
          show_colnames = F
)
pheatmap(t(ihcHeatT_tumor_norm3[,grep("_S", colnames(ihcHeatT_tumor_norm3))]), clustering_method = "ward.D2",
          clustering_distance_rows = "euclidean", clustering_distance_cols = "euclidean",
          cutree_cols = 3,
          #color = colorRampPalette(brewer.pal(n = 7, name = "YlGnBu"))(100),
          annotation_col = ihcAnno,
          annotation_colors = ihcAnno_color,
          fontsize = 5,
          show_colnames = F
)
pheatmap(t(ihcHeatT_tumor_norm3[,grep("_T", colnames(ihcHeatT_tumor_norm3))]), clustering_method = "ward.D2",
          clustering_distance_rows = "euclidean", clustering_distance_cols = "euclidean",
          cutree_cols = 2,
          #color = colorRampPalette(brewer.pal(n = 7, name = "YlGnBu"))(100),
          annotation_col = ihcAnno,
          annotation_colors = ihcAnno_color,
          fontsize = 5,
          show_colnames = F
)
```

```{r}
fig5d_T <- pheatmap(t(ihcHeatT_tumor_norm3[,grep("_T", colnames(ihcHeatT_tumor_norm3))]), clustering_method = "ward.D2",
          clustering_distance_rows = "euclidean", clustering_distance_cols = "euclidean",
          cutree_cols = 2,
          #color = colorRampPalette(brewer.pal(n = 7, name = "YlGnBu"))(100),
          annotation_col = ihcAnno,
          annotation_colors = ihcAnno_color,
          fontsize = 5,
          show_colnames = F
)
fig5d_T
```

```{r}
fig5d_T.cluster <- fig5d_T$tree_col

fig5d_T.cluster.plot <- plot(fig5d_T.cluster, hang = -1, cex=0.6, axes=FALSE, ann=FALSE)

fig5d_T.cut <- cutree(fig5d_T.cluster, 2)

fig5d_T.cut_1 <- names(fig5d_T.cut)[fig5d_T.cut == 1]
fig5d_T.cut_2 <- names(fig5d_T.cut)[fig5d_T.cut == 2]

fig5d_T.cut_gini <- merge(AB_Gini_anno, fig5d_T.cut, by = "row.names", all.y = TRUE)

fig5d_T.cut_gini <- as.data.frame(fig5d_T.cut_gini, row.names = fig5d_T.cut_gini$Row.names)

fig5d_T.cut_gini$Row.names <- NULL

fig5d_T.cut_gini <- rename(fig5d_T.cut_gini, c("Cluster"="y"))
```

```{r}
fig5d_T.cut_gini[fig5d_T.cut_gini$Cluster == 1, "Class"] <- "Immune cold"
fig5d_T.cut_gini[fig5d_T.cut_gini$Cluster == 2, "Class"] <- "Immune hot"
fig5d_T.cut_gini$Class = factor(fig5d_T.cut_gini$Class, levels = c("Immune hot", "Immune cold"))
```

```{r}
fig5d_T.cut_gini <- as.data.frame(fig5d_T.cut_gini)
fig5d_T.cut_gini <- rename(fig5d_T.cut_gini, c("GiniIndex"="Gini index"))
compare_means(GiniIndex ~ Class, data = fig5d_T.cut_gini)
```

```{r}
fig5d_T.box <- ggplot(fig5d_T.cut_gini, aes(x = Class, y = GiniIndex, fill=Class)) +
    geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
    theme_classic() +
    # coord_flip() +
    scale_fill_manual(values = c("#e64b35", "#4dbbd5")) +
    labs(title = "Class", x = "Class") +
    theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
    stat_compare_means(paired = NULL)

fig5d_T.box
```

```{r}
fig5d_T.cut_km <- merge(fig5d_T.cut_gini, GiniClin_tumor[, c("OS_years_5_years_trunk_20190329", "status_5_years_trunk_20190329")], by = "row.names")
fig5d_T.cut_km <- as.data.frame(fig5d_T.cut_km, row.names = fig5d_T.cut_km$Row.names)
fig5d_T.cut_km$Row.names <- NULL

fig5d_T.test <- survfit(Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~ Class, data = fig5d_T.cut_km)

summary(fig5d_T.test)$table
```

```{r}
ggsurvplot(fig5d_T.test,
         pval = TRUE,
         palette = c("#e64b35", "#4dbbd5", "#999999"),
         break.time.by = 1,
         ggtheme = theme_classic(),
         #legend.labs = c("Above median", "Under median"),
         risk.table = "abs_pct",
         risk.table.y.text = FALSE,
         xlab = "Time (years)",
         title = "giniSurv_Class",
         xlim = c(0,5.2),
         axes.offset = FALSE,
         risk.table.fontsize = 2.5,
         legend = c(0.9,0.9),
         risk.table.title = "Number at risk by time: n (%)",
         font.main = c())
```


```{r}
fig5d_S <- pheatmap(t(ihcHeatT_tumor_norm3[,grep("_S", colnames(ihcHeatT_tumor_norm3))]), clustering_method = "ward.D2",
        clustering_distance_rows = "euclidean", clustering_distance_cols = "euclidean",
        cutree_cols = 3,
        #color = colorRampPalette(brewer.pal(n = 7, name = "YlGnBu"))(100),
        annotation_col = ihcAnno,
        annotation_colors = ihcAnno_color,
        fontsize = 5,
        show_colnames = F
)
fig5d_S
```

```{r}
fig5d_S.cluster <- fig5d_S$tree_col

fig5d_S.cluster.plot <- plot(fig5d_S.cluster, hang = -1, cex=0.6, axes=FALSE, ann=FALSE)

fig5d_S.cut <- cutree(fig5d_S.cluster, 3)

fig5d_S.cut_1 <- names(fig5d_S.cut)[fig5d_S.cut == 1]
fig5d_S.cut_2 <- names(fig5d_S.cut)[fig5d_S.cut == 2]
fig5d_S.cut_3 <- names(fig5d_S.cut)[fig5d_S.cut == 3]

fig5d_S.cut_gini <- merge(AB_Gini_anno, fig5d_S.cut, by = "row.names", all.y = TRUE)

fig5d_S.cut_gini <- as.data.frame(fig5d_S.cut_gini, row.names = fig5d_S.cut_gini$Row.names)

fig5d_S.cut_gini$Row.names <- NULL

fig5d_S.cut_gini <- rename(fig5d_S.cut_gini, c("Cluster"="y"))
```

```{r}
fig5d_S.cut_gini[fig5d_S.cut_gini$Cluster == 1, "Class"] <- "Immune cold"
fig5d_S.cut_gini[fig5d_S.cut_gini$Cluster == 2, "Class"] <- "Median"
fig5d_S.cut_gini[fig5d_S.cut_gini$Cluster == 3, "Class"] <- "Immune hot"
fig5d_S.cut_gini$Class = factor(fig5d_S.cut_gini$Class, levels = c("Immune hot", "Median", "Immune cold"))
```

```{r}
fig5d_S.cut_gini <- as.data.frame(fig5d_S.cut_gini)
fig5d_S.cut_gini <- rename(fig5d_S.cut_gini, c("GiniIndex"="Gini index"))
compare_means(GiniIndex ~ Class, data = fig5d_S.cut_gini)
```

```{r}
fig5d_S.box <- ggplot(fig5d_S.cut_gini, aes(x = Class, y = GiniIndex, fill=Class)) +
    geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
    theme_classic() +
    # coord_flip() +
    scale_fill_manual(values = c("#e64b35", "#4dbbd5", "#999999")) +
    labs(title = "Class", x = "Class") +
    theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
    stat_compare_means(paired = NULL)
```

```{r}
fig5d_S.cut_km <- merge(fig5d_S.cut_gini, GiniClin_tumor[, c("OS_years_5_years_trunk_20190329", "status_5_years_trunk_20190329")], by = "row.names")
fig5d_S.cut_km <- as.data.frame(fig5d_S.cut_km, row.names = fig5d_S.cut_km$Row.names)
fig5d_S.cut_km$Row.names <- NULL

fig5d_S.test <- survfit(Surv(OS_years_5_years_trunk_20190329, status_5_years_trunk_20190329) ~ Class, data = fig5d_S.cut_km)

summary(fig5d_S.test)$table
```

```{r}
ggsurvplot(fig5d_S.test,
         pval = TRUE,
         palette = c("#e64b35", "#4dbbd5", "#999999"),
         break.time.by = 1,
         ggtheme = theme_classic(),
         #legend.labs = c("Above median", "Under median"),
         risk.table = "abs_pct",
         risk.table.y.text = FALSE,
         xlab = "Time (years)",
         title = "giniSurv_Class",
         xlim = c(0,5.2),
         axes.offset = FALSE,
         risk.table.fontsize = 2.5,
         legend = c(0.9,0.9),
         risk.table.title = "Number at risk by time: n (%)",
         font.main = c())
```


```{r}
ihcHeatT_tumor_norm4 <- ihcHeatT.norm3[rownames(ihcHeatT_tumor_norm2),grep("SAndTM", colnames(ihcHeatT.norm3))]

ihcHeatT_tumor_norm4 <- merge(AB_Gini_anno, ihcHeatT_tumor_norm4, by="row.names", all=FALSE)

ihcHeatT_tumor_norm4 <- as.data.frame(ihcHeatT_tumor_norm4, row.names = ihcHeatT_tumor_norm4$Row.names)
ihcHeatT_tumor_norm4$Row.names <- NULL

ihcHeatT_tumor_norm4 <- rename(ihcHeatT_tumor_norm4, c("GiniIndex"="Gini index"))

ihcHeatT_tumor_norm4 <- ihcHeatT_tumor_norm4 %>% rename_with(~ gsub("SQ1", "_",.x))

ihcHeatT_tumor_norm4 <- ihcHeatT_tumor_norm4[,grep("CD44", colnames(ihcHeatT_tumor_norm4), invert = TRUE)]
```

```{r}
fig5d_ST <- pheatmap(t(ihcHeatT_tumor_norm4[,c(2:12)]), clustering_method = "ward.D2",
        clustering_distance_rows = "euclidean", clustering_distance_cols = "euclidean",
        cutree_cols = 2,
        #color = colorRampPalette(brewer.pal(n = 7, name = "YlGnBu"))(100),
        annotation_col = ihcAnno,
        annotation_colors = ihcAnno_color,
        fontsize = 5,
        show_colnames = F)

fig5d_ST
```

```{r}
fig5d_ST.cluster <- fig5d_ST$tree_col

fig5d_ST.cluster.plot <- plot(fig5d_ST.cluster, hang = -1, cex=0.6, axes=FALSE, ann=FALSE)

#plot(fig5d_ST.cluster, hang = -1, cex=0.6, axes=FALSE, ann=FALSE)

fig5d_ST.cut <- cutree(fig5d_ST.cluster, 2)

fig5d_ST.cut_1 <- names(fig5d_ST.cut)[fig5d_ST.cut == 1]
fig5d_ST.cut_2 <- names(fig5d_ST.cut)[fig5d_ST.cut == 2]

fig5d_ST.cut_gini <- merge(AB_Gini_anno, fig5d_ST.cut, by = "row.names", all.y = TRUE)

fig5d_ST.cut_gini <- as.data.frame(fig5d_ST.cut_gini, row.names = fig5d_ST.cut_gini$Row.names)

fig5d_ST.cut_gini$Row.names <- NULL

fig5d_ST.cut_gini <- rename(fig5d_ST.cut_gini, c("Cluster"="y"))
```

```{r}
fig5d_ST.cut_gini[fig5d_ST.cut_gini$Cluster == 1, "Class"] <- "Immune cold"
fig5d_ST.cut_gini[fig5d_ST.cut_gini$Cluster == 2, "Class"] <- "Immune hot"
fig5d_ST.cut_gini$Class = factor(fig5d_ST.cut_gini$Class, levels = c("Immune hot", "Immune cold"))
```

```{r}
fig5d_ST.cut_gini <- as.data.frame(fig5d_ST.cut_gini)
fig5d_ST.cut_gini <- rename(fig5d_ST.cut_gini, c("GiniIndex"="Gini index"))
compare_means(GiniIndex ~ Class, data = fig5d_ST.cut_gini)
```

```{r}
fig5d_ST.box <- ggplot(fig5d_ST.cut_gini, aes(x = Class, y = GiniIndex, fill=Class)) +
    geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black") +
    theme_classic() +
    # coord_flip() +
    scale_fill_manual(values = c("#e64b35", "#4dbbd5", "#999999")) +
    labs(title = "Class", x = "Class") +
    theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
    stat_compare_means(paired = NULL)
fig5d_ST.box
```
## Ordered by Gini
```{r}
pheatmap(t(ihcHeatT_tumor_norm4[order(ihcHeatT_tumor_norm4$GiniIndex),c(2:12)]),
         #clustering_method = "ward.D2",
         cluster_rows = F, cluster_cols = F,
         cutree_cols = 2,
         #color = colorRampPalette(brewer.pal(n = 7, name = "YlGnBu"))(100),
         annotation_col = ihcAnno,
         annotation_colors = ihcAnno_color,
         fontsize = 8,
         show_colnames = F)
```

# IHC correlation plot

```{r}
fig6 <- corrplot(data.matrix(ihcCorR), method = "circle",col = rev(COL2('RdBu', 200)),
                 p.mat = data.matrix(ihcCorP), insig = "label_sig", sig.level = c(.001, .01, .05), pch.cex = 0.8,
                 tl.col = "black")
fig6
```

```{r}
ihcCorP_melt <- as.data.frame(ihcCorP_melt)
ihcCorP_melt$var1 <- factor(ihcCorP_melt$var1, levels = c("CD3","CD4","CD8","CD20","CD44","CD45RO","CD138","CD163","FOXP3","NKp46","PD1","PDL1"))
ihcCorP_melt$var2 <- factor(ihcCorP_melt$var2, levels = c("SAndT","Tumor", "Stroma"))

fig6b <- ggplot(ihcCorP_melt, aes(var1, var2, fill = Corr)) +
  geom_point(shape=21, colour = "black", size = 12)+
  theme_classic()+
  scale_fill_gradientn(colours = rev(brewer.pal(5, "Spectral")))+
  labs(title = "Correlation", x = "IHC marker", y = "Position") +
  theme(axis.text = element_text(size = 10, colour = "black"), axis.title = element_text(size = 10),plot.title = element_text(size=15))+
  geom_text(aes(label = label), size = 5, colour = "black")

fig6b
```


# Figure 9 Lymphotrack cluster plot

## L481
```{r}
L481_L <- as.data.frame(L481_L)

L481_R <- as.data.frame(L481_R)

L481_all <- as.data.frame(L481_all)

L481_L["Class"] <- "Lymphotrack"

L481_R["Class"] <- "RNAseq"
```

```{r}
npj.pal = pal_npg("nrc", alpha = 0.9)(10)
simp.pal = pal_simpsons("springfield")(16)
nejm.pal = pal_nejm("default", alpha = 0.9)(8)
jama.pal = pal_jama("default",alpha = 0.9)(7)
fig9_pal = c(c("#E0E3E3FF"), npj.pal, simp.pal, nejm.pal, jama.pal)

fig9_L481 <- ggplot(L481_all, aes(x= Type, y= Fraction, fill= Clone)) +
  geom_col(position = "stack", width = 0.6) +
  theme_classic() +
  scale_y_continuous(expand = c(0,0)) +
  scale_fill_manual(values = fig9_pal) +
  scale_y_break(c(40,95), scales = 0.2, ticklabels = c(95,100))


gg.gap(plot = fig9_L481,
       segments = c(40,80),
       ylim = c(0, 100),
       tick_width = c(5, 10),
       rel_heights = c(0.3, 0, 0.1)
       )

ggplot(L481_L, aes(x=Class, y= Fraction, fill= reorder(Lymphotrack, -Fraction))) +
     geom_col(position = "stack", width = 0.6) +
     theme_classic() +
     scale_fill_manual(values = fig9_pal) +
     scale_y_break(c(7,92), scales = 0.2, ticklabels = c(95,100), expand = FALSE) +
     scale_y_continuous(expand = c(0,0), breaks = c(0,2,4,6))

ggplot(L481_R, aes(x=Class, y= Fraction, fill= reorder(RNAseq, -Fraction))) +
     geom_col(position = "stack", width = 0.6) +
     theme_classic() +
     scale_fill_manual(values = fig9_pal) +
     scale_y_break(c(40,92), scales = 0.2, ticklabels = c(95,100), expand = FALSE) +
     scale_y_continuous(expand = c(0,0), breaks = c(0,10,20,30,40))

```


# Fig10 Vocanol plot

```{r}
diffGene <- read.table('/Users/huiyu818/Project/TCR/DifferentialExpr/bomi2_fullResult.txt', sep = "\t", header = TRUE, row.names = 1)

# Sort the table according to the padj(adjusted p-value) ascending.
# For genes with same padj, order according to the log2FC descending.
# Then , at the end of the coding is essensial
diffGene <- diffGene[order(diffGene$padj, diffGene$log2FoldChange, decreasing = c(FALSE, TRUE)),]

library('biomaRt')

mart <- useDataset("hsapiens_gene_ensembl", useMart("ensembl"))

diff_genes_eid <- rownames(diffGene)

diff_genes_G_list <- getBM(filters="ensembl_gene_id", attributes=c("ensembl_gene_id", "hgnc_symbol", "description"), values=diff_genes_eid, mart=mart)

# diffGene_hgnc <- merge(diffGene, diff_genes_G_list, by.x="row.names", by.y = "ensembl_gene_id")

# The significantly up-regulated genes are labeled with 'UP'
diffGene[which(diffGene$log2FoldChange >= 1 & diffGene$padj < 0.01), 'sign'] <- 'UP'

# The significantly down-regulated genes are labeled with 'DOWN'
diffGene[which(diffGene$log2FoldChange <= -1 & diffGene$padj < 0.01), 'sign'] <- 'DOWN'

# The rest genes are labeled 'none'
diffGene[which(abs(diffGene$log2FoldChange) <= 1 | diffGene$padj >= 0.01), 'sign'] <- 'none'

diffGene["ENSG00000180644", "label"] <- "PRF1"
diffGene["ENSG00000100453", "label"] <- "GZMB"
diffGene["ENSG00000100450", "label"] <- "GZMH"
diffGene["ENSG00000120217", "label"] <- "PD-L1"
diffGene["ENSG00000089692", "label"] <- "LAG3"
diffGene["ENSG00000181847", "label"] <- "TIGIT"
diffGene[diff_genes_G_list[diff_genes_G_list$hgnc_symbol == "IFNG","ensembl_gene_id"], "label"] <- "IFNG"
diffGene[diff_genes_G_list[diff_genes_G_list$hgnc_symbol == "IL17A","ensembl_gene_id"], "label"] <- "IL17A"
diffGene[diff_genes_G_list[diff_genes_G_list$hgnc_symbol == "NKG7","ensembl_gene_id"], "label"] <- "NKG7"
diffGene[diff_genes_G_list[diff_genes_G_list$hgnc_symbol == "FASLG","ensembl_gene_id"], "label"] <- "FASLG"
diffGene[diff_genes_G_list[diff_genes_G_list$hgnc_symbol == "GZMA","ensembl_gene_id"], "label"] <- "GZMA"
diffGene[diff_genes_G_list[diff_genes_G_list$hgnc_symbol == "NCR1","ensembl_gene_id"], "label"] <- "NCR1"
diffGene[diff_genes_G_list[diff_genes_G_list$hgnc_symbol == "GZMM","ensembl_gene_id"], "label"] <- "GZMM"

library(ggrepel)

fig10 <- ggplot(data = diffGene, aes(x = log2FoldChange, y = -log10(padj), color = sign)) + 
  geom_point(size = 1) +  #Create scatter plot
  scale_color_manual(values = c('red', 'gray', 'green'), limits = c('UP', 'none', 'DOWN')) +  #Set the color
  labs(x = 'log2 Fold Change', y = '-log10 adjust p-value', title = 'High clonality vs Low clonality', color = '') +  #Set the labels
  theme(plot.title = element_text(hjust = 0.5, size = 14), panel.grid = element_blank(), #Change the them and grid lines
  panel.background = element_rect(color = 'black', fill = 'transparent'), 
  legend.key = element_rect(fill = 'transparent')) +
  geom_vline(xintercept = c(-1, 1), lty = 3, color = 'black') +  #Add threshold line
  geom_hline(yintercept = 2, lty = 3, color = 'black') +
  xlim(-12, 12) + ylim(0, 35) + #Set the curve boundary
  geom_text_repel(
    data = diffGene,
    aes(label = label),
    size = 3, segment.color = "blue", show.legned = FALSE,
    point.padding = unit(0.8, "lines")
  )

fig10
```


#Figure11 Phenotype

```{r}
library(Hmisc)

library(corrplot)

library(readxl)

bomi2_pheno <- read_excel("~/Project/BOMI2/bomi2_phenotyped_densities.xlsx", sheet = "phenotyped_densities")

View(bomi2_pheno)

bomi2_pheno <- as.data.frame(bomi2_pheno, row.names = bomi2_pheno$ID)

phenoClin <- merge(GiniClin_tumor, bomi2_pheno, by = "ID", sort = TRUE)

phenoClin <- as.data.frame(phenoClin, row.names = phenoClin$ID)

write.csv(phenoClin, file = "/Users/huiyu818/Project/BOMI2/bomi2_phenotyped_densities_clin.csv")

phenoClin_gini <- merge(GiniClin_tumor[, c("ID", "GiniIndex", "giniGroup")], bomi2_pheno, by="ID", sort = TRUE)

phenoClin_gini <- as.data.frame(phenoClin_gini, row.names = phenoClin_gini$ID)
phenoClin_gini$ID <- NULL

phenoClin_gini2 <- subset(phenoClin_gini, select = -giniGroup)

phenoClin_gini2 <- as.data.frame(phenoClin_gini2)

phenoClin_corr <- rcorr(as.matrix(phenoClin_gini2), type = "spearman")

write.csv(phenoClin_corr$r, file = "~/Project/TCR_Summary/Figure11/phenoCor.csv")
write.csv(phenoClin_corr$P, file = "~/Project/TCR_Summary/Figure11/phenoPvalue.csv")

cor(phenoClin_gini2, use = "pairwise.complete.obs", method = "spearman")
# recorr and cor fits well

phenoCor_melt <- read_excel("~/Project/TCR_Summary/Figure11/giniPheno.xlsx", sheet = "merge")

phenoCor_melt <- as.data.frame(phenoCor_melt)

phenoCor_melt$var1 <- factor(phenoCor_melt$var1, levels = c("CD4 Eff","CD4 Treg","CD8 Eff","CD8 Treg","B-cell","NK-cell","NKT-cell","M1","M2","CD163","iDC","mDC","pDC"))

phenoCor_melt$var2 <- factor(phenoCor_melt$var2, levels = c("SAndT","Tumor", "Stroma"))

fig11 <- ggplot(phenoCor_melt, aes(var1, var2, fill = Corr)) +
  geom_point(shape=21, colour = "black", size = 12)+
  theme_classic()+
  scale_fill_gradientn(colours = rev(brewer.pal(5, "Spectral")))+
  labs(title = "Correlation", x = "Cell phenotypes", y = "Position") +
  theme(axis.text = element_text(size = 10, colour = "black"), axis.title = element_text(size = 10),plot.title = element_text(size=15))+
  geom_text(aes(label = label), size = 5, colour = "black")

fig11
```

## Cluster
### All gini

This method start from the t cell pheno type data to check if the gini index is linked with these group. But there is also a angle that start from Gini index (25%) to analyze the t cell composition changes
```{r}

# ihcHeatT_tumor_norm4 <- ihcHeatT.norm3[rownames(ihcHeatT_tumor_norm2),grep("SAndTM", colnames(ihcHeatT.norm3))]
# 
# ihcHeatT_tumor_norm4 <- merge(AB_Gini_anno, ihcHeatT_tumor_norm4, by="row.names", all=FALSE)
# 
# ihcHeatT_tumor_norm4 <- as.data.frame(ihcHeatT_tumor_norm4, row.names = ihcHeatT_tumor_norm4$Row.names)
# ihcHeatT_tumor_norm4$Row.names <- NULL
# 
# ihcHeatT_tumor_norm4 <- rename(ihcHeatT_tumor_norm4, c("GiniIndex"="Gini index"))
# 
# ihcHeatT_tumor_norm4 <- ihcHeatT_tumor_norm4 %>% rename_with(~ gsub("SQ1", "_",.x))
# 
# ihcHeatT_tumor_norm4 <- ihcHeatT_tumor_norm4[,grep("CD44", colnames(ihcHeatT_tumor_norm4), invert = TRUE)]

phenoClin_gini3 <- phenoClin_gini2[, grep("Pan", colnames(phenoClin_gini2), invert = T)]

phenoClin_gini3.norm <- cbind(phenoClin_gini3[,1],scale(phenoClin_gini3[,c(2:40)], center = T, scale = T))
phenoClin_gini3.norm <- as.data.frame(phenoClin_gini3.norm)
colnames(phenoClin_gini3.norm)[colnames(phenoClin_gini3.norm) == "V1"] = "GiniIndex"
```

```{r}
min.max.norm <- function(x){
  (x - min(x, na.rm = TRUE))/(max(x, na.rm = TRUE) - min(x, na.rm = TRUE))
}

phenoClin_gini3.norm2 <- apply(phenoClin_gini3[, c(2:40)], 2, min.max.norm)
phenoClin_gini3.norm2 <- as.data.frame(phenoClin_gini3.norm2)
phenoClin_gini3.norm2 <- cbind(phenoClin_gini3[,1], phenoClin_gini3.norm2)
colnames(phenoClin_gini3.norm2)[colnames(phenoClin_gini3.norm2) == "phenoClin_gini3[, 1]"] = "GiniIndex"
```


```{r}
pheatmap(t(phenoClin_gini2[,grep("st_", colnames(phenoClin_gini2), invert = T)]), clustering_method = "ward.D2",
        clustering_distance_rows = "euclidean", clustering_distance_cols = "euclidean",
        cutree_cols = 2,
        #color = colorRampPalette(brewer.pal(n = 7, name = "YlGnBu"))(100),
        annotation_col = ihcAnno,
        annotation_colors = ihcAnno_color,
        fontsize = 5,
        show_colnames = F)

pheatmap(t(phenoClin_gini3.norm[,c(1,grep("st_", colnames(phenoClin_gini3.norm[,c(2:16)]), invert = T)+1)]), clustering_method = "ward.D2",
         clustering_distance_rows = "euclidean", clustering_distance_cols = "euclidean",
         cutree_cols = 2,
         #color = colorRampPalette(brewer.pal(n = 7, name = "YlGnBu"))(100),
         annotation_col = ihcAnno,
         annotation_colors = ihcAnno_color,
         fontsize = 5,
         show_colnames = F)
pheatmap(t(phenoClin_gini3.norm2[,grep("st_", colnames(phenoClin_gini3.norm2[,c(1:16)]), invert = T)]), clustering_method = "ward.D2",
         clustering_distance_rows = "euclidean", clustering_distance_cols = "euclidean",
         cutree_cols = 2,
         #color = colorRampPalette(brewer.pal(n = 7, name = "YlGnBu"))(100),
         annotation_col = ihcAnno,
         annotation_colors = ihcAnno_color,
         fontsize = 5,
         show_colnames = F)

pheatmap(t(phenoClin_gini3.norm2[,grep("st_", colnames(phenoClin_gini3.norm2[,c(2:16)]))]), clustering_method = "ward.D2",
          cluster_rows = F, clustering_distance_cols = "euclidean",
          cutree_cols = 5,
          #color = colorRampPalette(brewer.pal(n = 9, name = "YlOrBr"))(100),
          annotation_col = ihcAnno,
          annotation_colors = ihcAnno_color,
          fontsize = 5,
          show_colnames = F)
```

```{r}
fig11b <- pheatmap(t(phenoClin_gini3.norm2[,c(1,grep("st_", colnames(phenoClin_gini3.norm2[,c(2:16)]))+1)]), clustering_method = "ward.D2",
          cluster_rows = F, clustering_distance_cols = "euclidean",
          cutree_cols = 3,
          #color = colorRampPalette(brewer.pal(n = 9, name = "YlOrBr"))(100),
          annotation_col = ihcAnno,
          annotation_colors = ihcAnno_color,
          fontsize = 5,
          show_colnames = F)
fig11b
# pheatmap(t(phenoClin_gini3.norm2[,c(1,grep("st_", colnames(phenoClin_gini3.norm2[,c(2:16)]))+1)]), clustering_method = "ward.D2",
#          cluster_rows = F, clustering_distance_cols = "euclidean",
#          cutree_cols = 6,
#          #color = colorRampPalette(brewer.pal(n = 9, name = "YlOrBr"))(100),
#          annotation_col = ihcAnno,
#          annotation_colors = ihcAnno_color,
#          fontsize = 10,
#          show_colnames = F)
```

```{r}
pheatmap(t(phenoClin_gini3.norm2[,grep("st_", colnames(phenoClin_gini3.norm2[,c(2:13)]))+1]), clustering_method = "ward.D2",
         cluster_rows = F, clustering_distance_cols = "euclidean",
         cutree_cols = 4,
         #color = colorRampPalette(brewer.pal(n = 9, name = "YlOrBr"))(100),
         annotation_col = ihcAnno,
         annotation_colors = ihcAnno_color,
         fontsize = 10,
         show_colnames = F)
```


```{r}
fig11b.cluster <- fig11b$tree_col

fig11b.cluster.plot <- plot(fig11b.cluster, hang = -1, cex=0.6, axes=FALSE, ann=FALSE)

fig11b.cut <- cutree(fig5d_T.cluster, 3)

# fig5d_T.cut_1 <- names(fig5d_T.cut)[fig5d_T.cut == 1]
# fig5d_T.cut_2 <- names(fig5d_T.cut)[fig5d_T.cut == 2]
# 
# fig5d_T.cut_gini <- merge(AB_Gini_anno, fig5d_T.cut, by = "row.names", all.y = TRUE)
# 
# fig5d_T.cut_gini <- as.data.frame(fig5d_T.cut_gini, row.names = fig5d_T.cut_gini$Row.names)
# 
# fig5d_T.cut_gini$Row.names <- NULL
# 
# fig5d_T.cut_gini <- rename(fig5d_T.cut_gini, c("Cluster"="y"))
```

```{r}
fig11c <- pheatmap(t(phenoClin_gini3.norm2[!(rownames(phenoClin_gini3.norm2) == "L464T"),grep("st_", colnames(phenoClin_gini3.norm2[,c(1:14)]))]),
                   clustering_method = "ward.D2",
                   cluster_rows = F, clustering_distance_cols = "euclidean",
                   cutree_cols = 4,
                   #color = colorRampPalette(brewer.pal(n = 9, name = "YlOrBr"))(100),
                   annotation_col = ihcAnno,
                   annotation_colors = ihcAnno_color,
                   fontsize = 5,
                   show_colnames = F)
```

```{r}
fig11c.cluster <- fig11c$tree_col

fig11c.cluster.plot <- plot(fig11c.cluster, hang = -1, cex=0.6, axes=FALSE, ann=FALSE)

fig11c.cut <- cutree(fig11c.cluster, 4)
```

```{r}
fig11c.cut_gini <- merge(phenoClin_gini3.norm2[!(rownames(phenoClin_gini3.norm2) == "L464T"),c(1,grep("st_", colnames(phenoClin_gini3.norm2[,c(1:14)])))],
                         fig11c.cut, by = "row.names", all.y = TRUE)
fig11c.cut_gini <- as.data.frame(fig11c.cut_gini, row.names = fig11c.cut_gini$Row.names)
fig11c.cut_gini$Row.names <- NULL
colnames(fig11c.cut_gini)[colnames(fig11c.cut_gini) == "y"] <- "CutGroup"
fig11c.cut_gini$CutGroup = factor(fig11c.cut_gini$CutGroup)
```

```{r}
ggplot(fig11c.cut_gini, aes(x = CutGroup, y = GiniIndex, fill=CutGroup)) +
  geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black", notch = FALSE) +
  theme_classic() +
  #coord_flip() +
  #scale_color_npg() +
  labs(title = "Gini", x = "Group") +
  theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
  stat_compare_means(paired = NULL)
```
```{r}
ggplot(fig11c.cut_gini, aes(x = CutGroup, y = s_CD4_Treg, fill=CutGroup)) +
  geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black", notch = FALSE) +
  theme_classic() +
  #coord_flip() +
  #scale_color_npg() +
  labs(title = "CD4_Treg", x = "Group") +
  theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
  stat_compare_means(paired = NULL)
```
```{r}
ggplot(fig11c.cut_gini, aes(x = CutGroup, y = st_CD4_Eff, fill=CutGroup)) +
  geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black", notch = FALSE) +
  theme_classic() +
  #coord_flip() +
  #scale_color_npg() +
  labs(title = "CD4_Eff", x = "Group") +
  theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
  stat_compare_means(paired = NULL)
```

```{r}
fig11c.cut_gini.melt <- melt(fig11c.cut_gini, na.rm = F)

ggplot(fig11c.cut_gini.melt, aes(x = variable, y = value, fill=CutGroup)) +
  geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black", notch = FALSE) +
  theme_classic() +
  #coord_flip() +
  scale_fill_npg(palette = c("nrc"), alpha=1) +
  labs(x = "T cell phenotype") +
  theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))
  #stat_compare_means(paired = NULL)
```
```{r}
ggplot(fig11c.cut_gini.melt, aes(x = variable, y = value, fill=variable)) +
  geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black", notch = FALSE) +
  theme_classic() +
  #coord_flip() +
  scale_fill_npg(palette = c("nrc"), alpha=1) +
  labs(x = "T cell phenotype") +
  theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
  facet_wrap(~CutGroup,ncol = 1)
```

### Smooth line
```{r}
fig11c.cut_gini.melt2 <- melt(cbind("ID"=rownames(fig11c.cut_gini),fig11c.cut_gini[,c(1:5)]), na.rm = F, id.vars = c("ID", "GiniIndex"), variable.factor =F)

fig11c.cut_gini.melt2$variable <- as.factor(fig11c.cut_gini.melt2$variable)

# Smooth value trend line
ggplot(fig11c.cut_gini.melt2, aes(x = GiniIndex, y = value)) +
  geom_smooth(aes(color=variable,fill=variable), method = "loess") +
  theme_classic() +
  #coord_flip() +
  scale_color_npg(palette = c("nrc"), alpha=1) +
  scale_fill_npg(palette = c("nrc"), alpha=1) +
  labs(x = "Gini coefficient") +
  theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))

# Raw stacking value
ggplot(fig11c.cut_gini.melt2, aes(x = GiniIndex, y = value,fill=variable)) +
  geom_area() +
  theme_classic() +
  #coord_flip() +
  scale_color_npg(palette = c("nrc"), alpha=1) +
  scale_fill_npg(palette = c("nrc"), alpha=1) +
  labs(x = "Gini coefficient") +
  theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))

# Raw fraction change
ggplot(fig11c.cut_gini.melt2, aes(x = GiniIndex, y = value,fill=variable)) +
    geom_area(position = "fill") +
    theme_classic() +
    #coord_flip() +
    scale_color_npg(palette = c("nrc"), alpha=0.8) +
    scale_fill_npg(palette = c("nrc"), alpha=0.8) +
    labs(x = "Gini coefficient") +
    theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))

# smooth density value
ggplot(fig11c.cut_gini.melt2, aes(x = GiniIndex, y = value,fill=variable)) +
  geom_area(fill=NA) +
  stat_smooth(geom = "area", method = "loess", span = 1/3, alpha=0.4)+
  theme_classic() +
  #coord_flip() +
  scale_color_npg(palette = c("nrc"), alpha=1) +
  scale_fill_npg(palette = c("nrc"), alpha=1) +
  labs(x = "Gini coefficient") +
  theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))

# Smooth stacking density value
ggplot(fig11c.cut_gini.melt2, aes(x = GiniIndex, y = value)) +
    stat_smooth(aes(color=variable,fill=variable), method = "loess", geom="area", position = "stack",span = 0.5) +
    theme_classic() +
    #coord_flip() +
    scale_color_npg(palette = c("nrc"), alpha=1) +
    scale_fill_npg(palette = c("nrc"), alpha=0.7) +
    labs(x = "Gini coefficient") +
    theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))

# Smooth fraction changes
ggplot(fig11c.cut_gini.melt2, aes(x = GiniIndex, y = value)) +
    stat_smooth(aes(color=variable,fill=variable), method = "loess", geom="area", position = "fill",span = 0.3) +
    theme_classic() +
    #coord_flip() +
    scale_color_npg(palette = c("nrc"), alpha=1) +
    scale_fill_npg(palette = c("nrc"), alpha=0.7) +
    labs(x = "Gini coefficient") +
    theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))+
    scale_x_continuous(expand = c(0,0), limits = c(0,0.8)) + scale_y_continuous(expand = c(0,0), limits = c(0,1))


# Raw fraction change of unnormalized data
phenoClin_gini3.melt <- melt(cbind("ID"=rownames(phenoClin_gini3),phenoClin_gini3[,c(1,grep("st_", colnames(phenoClin_gini3[,c(1:13)])))]),
                             na.rm = F, id.vars = c("ID", "GiniIndex"), variable.factor =F)

ggplot(phenoClin_gini3.melt, aes(x = GiniIndex, y = value,fill=variable)) +
    geom_area(position = "fill") +
    theme_classic() +
    #coord_flip() +
    scale_color_npg(palette = c("nrc"), alpha=0.8) +
    scale_fill_npg(palette = c("nrc"), alpha=0.8) +
    labs(x = "Gini coefficient") +
    theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))

# Raw stacking of unnormalized data
ggplot(phenoClin_gini3.melt, aes(x = GiniIndex, y = value,fill=variable)) +
    geom_area(position = "stack") +
    theme_classic() +
    #coord_flip() +
    scale_color_npg(palette = c("nrc"), alpha=0.8) +
    scale_fill_npg(palette = c("nrc"), alpha=0.8) +
    labs(x = "Gini coefficient") +
    theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))

# Smooth fraction change of unnormalized data
ggplot(phenoClin_gini3.melt, aes(x = GiniIndex, y = value)) +
    stat_smooth(aes(color=variable,fill=variable), method = "loess", geom="area", position = "fill",span = 0.3) +
    theme_classic() +
    #coord_flip() +
    scale_color_npg(palette = c("nrc"), alpha=1) +
    scale_fill_npg(palette = c("nrc"), alpha=0.7) +
    labs(x = "Gini coefficient") +
    theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))+
    scale_y_continuous(expand = c(0,0), limits = c(0,1))

# Smooth stacking unnormalized data
ggplot(phenoClin_gini3.melt, aes(x = GiniIndex, y = value)) +
    stat_smooth(aes(color=variable,fill=variable), method = "loess", geom="area", position = "stack",span = 0.3) +
    theme_classic() +
    #coord_flip() +
    scale_color_npg(palette = c("nrc"), alpha=1) +
    scale_fill_npg(palette = c("nrc"), alpha=0.7) +
    labs(x = "Gini coefficient") +
    theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))

```

```{r}
# Raw stacking plot with sampple density using 
fig11_gini <- ggplot(fig11c.cut_gini.melt2, aes(x = GiniIndex, y = 1)) + geom_vline(xintercept=fig11c.cut_gini.melt2[fig11c.cut_gini.melt2$variable == "st_CD4_Eff", "GiniIndex"]) + theme_classic() + theme(panel.background = element_blank())

# normalized raw stacking data
fig11e_normS <- ggplot(fig11c.cut_gini.melt2, aes(x = GiniIndex, y = value,fill=variable)) +
  geom_area() +
  theme_classic() +
  #coord_flip() +
  scale_color_npg(palette = c("nrc"), alpha=1) +
  scale_fill_npg(palette = c("nrc"), alpha=1) +
  labs(x = "Gini coefficient") +
  theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))

# raw stacking data
fig11e_rawS <- ggplot(phenoClin_gini3.melt, aes(x = GiniIndex, y = value,fill=variable)) +
  geom_area() +
  theme_classic() +
  #coord_flip() +
  scale_color_npg(palette = c("nrc"), alpha=1) +
  scale_fill_npg(palette = c("nrc"), alpha=1) +
  labs(x = "Gini coefficient") +
  theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))

# smoothed raw stacking data
fig11e_rawStackSmooth <- ggplot(phenoClin_gini3.melt, aes(x = GiniIndex, y = value)) +
    stat_smooth(aes(color=variable,fill=variable), method = "loess", geom="area", position = "stack",span = 0.4) +
    theme_classic() +
    #coord_flip() +
    scale_color_npg(palette = c("nrc"), alpha=1) +
    scale_fill_npg(palette = c("nrc"), alpha=0.9) +
    labs(x = "Gini coefficient") +
    theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))

# smoothed raw fraction change data
fig11e_rawSmooth <- ggplot(phenoClin_gini3.melt, aes(x = GiniIndex, y = value)) +
    stat_smooth(aes(color=variable,fill=variable), method = "loess", geom="area", position = "fill",span = 0.4) +
    theme_classic() +
    #coord_flip() +
    scale_color_npg(palette = c("nrc"), alpha=1) +
    scale_fill_npg(palette = c("nrc"), alpha=0.9) +
    labs(x = "Gini coefficient") +
    theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))

ggarrange(fig11e_rawS, fig11_gini, ncol = 1, nrow = 2, align = "hv")

ggarrange(fig11e_rawSmooth, fig11_gini, ncol = 1, nrow = 2, align = "hv")

ggarrange(fig11e_rawStackSmooth, fig11e_rawSmooth, fig11_gini, ncol = 1, nrow = 3, align = "hv", common.legend = T)

```


### Ordered by gini
```{r}
pheatmap(t(phenoClin_gini3.norm2[order(phenoClin_gini3.norm2$GiniIndex),c(2:40)]),
         clustering_method = "ward.D2",
         cluster_rows = F, clustering_distance_cols = "euclidean",
         cutree_cols = 4,
         #color = colorRampPalette(brewer.pal(n = 9, name = "YlOrBr"))(100),
         annotation_col = ihcAnno,
         annotation_colors = ihcAnno_color,
         fontsize = 5,
         show_colnames = F)

pheatmap(t(phenoClin_gini3.norm2[order(phenoClin_gini3.norm2$GiniIndex),c(17:31)]),
         clustering_method = "ward.D2",
         cluster_rows = F, cluster_cols = F,
         cutree_cols = 4,
         #color = colorRampPalette(brewer.pal(n = 9, name = "YlOrBr"))(100),
         annotation_col = ihcAnno,
         annotation_colors = ihcAnno_color,
         fontsize = 5,
         show_colnames = F)
```

### High gini group
```{r}
fig11d <- pheatmap(t(phenoClin_gini3.norm2[phenoClin_gini3.norm2$GiniIndex >= 0.25431,grep("st_", colnames(phenoClin_gini3.norm2[,c(1:14)]))]),
                   clustering_method = "ward.D2",
                   cluster_rows = F, clustering_distance_cols = "euclidean",
                   cutree_cols = 3,
                   #color = colorRampPalette(brewer.pal(n = 9, name = "YlOrBr"))(100),
                   annotation_col = ihcAnno,
                   annotation_colors = ihcAnno_color,
                   fontsize = 5,
                   show_colnames = F)

```

```{r}
fig11d.cluster <- fig11d$tree_col

fig11d.cluster.plot <- plot(fig11d.cluster, hang = -1, cex=0.6, axes=FALSE, ann=FALSE)

fig11d.cut <- cutree(fig11d.cluster, 3)
```
```{r}
fig11d.cut_gini <- merge(phenoClin_gini3.norm2[!(rownames(phenoClin_gini3.norm2) == "L464T"),c(1,grep("st_", colnames(phenoClin_gini3.norm2[,c(1:14)])))],
                         fig11d.cut, by = "row.names", all.y = TRUE)
fig11d.cut_gini <- as.data.frame(fig11d.cut_gini, row.names = fig11d.cut_gini$Row.names)
fig11d.cut_gini$Row.names <- NULL
colnames(fig11d.cut_gini)[colnames(fig11d.cut_gini) == "y"] <- "CutGroup"
fig11d.cut_gini$CutGroup = factor(fig11d.cut_gini$CutGroup)
```

```{r}
fig11d.cut_gini.melt <- melt(fig11d.cut_gini, na.rm = F)

ggplot(fig11d.cut_gini.melt, aes(x = variable, y = value, fill=CutGroup)) +
  geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black", notch = FALSE) +
  theme_classic() +
  #coord_flip() +
  scale_fill_npg(palette = c("nrc"), alpha=1) +
  labs(x = "T cell phenotype") +
  theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))
  #stat_compare_means(paired = NULL)
```

```{r}
# Raw stacking change of unnormalized data (Gini high group)
ggplot(phenoClin_gini3.melt[phenoClin_gini3.melt$GiniIndex >= 0.25431,], aes(x = GiniIndex, y = value,fill=variable)) +
    geom_area(position = "stack") +
    theme_classic() +
    #coord_flip() +
    scale_color_npg(palette = c("nrc"), alpha=0.8) +
    scale_fill_npg(palette = c("nrc"), alpha=0.8) +
    labs(x = "Gini coefficient") +
    theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))

# Smoothed stacking change of unnormalized data (Gini high group)
ggplot(phenoClin_gini3.melt[phenoClin_gini3.melt$GiniIndex >= 0.25431,], aes(x = GiniIndex, y = value)) +
    stat_smooth(aes(color=variable,fill=variable), method = "loess", geom="area", position = "stack",span = 0.3) +
    theme_classic() +
    #coord_flip() +
    scale_color_npg(palette = c("nrc"), alpha=1) +
    scale_fill_npg(palette = c("nrc"), alpha=0.7) +
    labs(x = "Gini coefficient") +
    theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))

# Raw fraction change of unnormalized data (Gini high group)
ggplot(phenoClin_gini3.melt[phenoClin_gini3.melt$GiniIndex >= 0.25431,], aes(x = GiniIndex, y = value,fill=variable)) +
    geom_area(position = "fill") +
    theme_classic() +
    #coord_flip() +
    scale_color_npg(palette = c("nrc"), alpha=0.8) +
    scale_fill_npg(palette = c("nrc"), alpha=0.8) +
    labs(x = "Gini coefficient") +
    theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))

# Smoothed fraction change of unnormalized data (Gini high group)
ggplot(phenoClin_gini3.melt[phenoClin_gini3.melt$GiniIndex >= 0.25431,], aes(x = GiniIndex, y = value)) +
    stat_smooth(aes(color=variable,fill=variable), method = "loess", geom="area", position = "fill",span = 0.3) +
    theme_classic() +
    #coord_flip() +
    scale_color_npg(palette = c("nrc"), alpha=1) +
    scale_fill_npg(palette = c("nrc"), alpha=0.7) +
    labs(x = "Gini coefficient") +
    theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))

# Raw stacking value (Gini high group)
ggplot(fig11c.cut_gini.melt2[fig11c.cut_gini.melt2$GiniIndex >= 0.25431,], aes(x = GiniIndex, y = value,fill=variable)) +
  geom_area() +
  theme_classic() +
  #coord_flip() +
  scale_color_npg(palette = c("nrc"), alpha=1) +
  scale_fill_npg(palette = c("nrc"), alpha=1) +
  labs(x = "Gini coefficient") +
  theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))

# Raw fraction value (Gini high group)
ggplot(fig11c.cut_gini.melt2[fig11c.cut_gini.melt2$GiniIndex >= 0.25431,], aes(x = GiniIndex, y = value,fill=variable)) +
    geom_area(position = "fill") +
    theme_classic() +
    #coord_flip() +
    scale_color_npg(palette = c("nrc"), alpha=0.8) +
    scale_fill_npg(palette = c("nrc"), alpha=0.8) +
    labs(x = "Gini coefficient") +
    theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))

# Smoothed stacking change (Gini high group)
ggplot(fig11c.cut_gini.melt2[fig11c.cut_gini.melt2$GiniIndex >= 0.25431,], aes(x = GiniIndex, y = value)) +
    stat_smooth(aes(color=variable,fill=variable), method = "loess", geom="area", position = "stack",span = 0.5) +
    theme_classic() +
    #coord_flip() +
    scale_color_npg(palette = c("nrc"), alpha=1) +
    scale_fill_npg(palette = c("nrc"), alpha=0.7) +
    labs(x = "Gini coefficient") +
    theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))

# Smoothed fraction change (Gini high group)
ggplot(fig11c.cut_gini.melt2[fig11c.cut_gini.melt2$GiniIndex >= 0.25431,], aes(x = GiniIndex, y = value)) +
    stat_smooth(aes(color=variable,fill=variable), method = "loess", geom="area", position = "fill",span = 0.5) +
    theme_classic() +
    #coord_flip() +
    scale_color_npg(palette = c("nrc"), alpha=1) +
    scale_fill_npg(palette = c("nrc"), alpha=0.7) +
    labs(x = "Gini coefficient") +
    theme(legend.position = "top",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20))


```


# Fig12
```{r}
PhenoAll <- read_excel("~/Project/BOMI2/bomi2_phenotyped_densities_sep.xlsx", sheet = "All")
PhenoAllNoCK <- read_excel("~/Project/BOMI2/bomi2_phenotyped_densities_sep.xlsx", sheet = "noCK")
PhenoTIL <- read_excel("~/Project/BOMI2/bomi2_phenotyped_densities_sep.xlsx", sheet = "TIL")
PhenoNK <- read_excel("~/Project/BOMI2/bomi2_phenotyped_densities_sep.xlsx", sheet = "NK")
PhenoAPC <- read_excel("~/Project/BOMI2/bomi2_phenotyped_densities_sep.xlsx", sheet = "APC")

PhenoAll <- as.data.frame(PhenoAll)
PhenoAllNoCK <- as.data.frame(PhenoAllNoCK)
PhenoTIL <- as.data.frame(PhenoTIL)
PhenoNK <- as.data.frame(PhenoNK)
PhenoAPC <- as.data.frame(PhenoAPC)

PhenoAll$Location <- factor(PhenoAll$Location, levels = c("Tumor", "Stroma", "SAndT"))
PhenoAllNoCK$Location <- factor(PhenoAllNoCK$Location, levels = c("Tumor", "Stroma", "SAndT"))
PhenoTIL$Location <- factor(PhenoTIL$Location, levels = c("Tumor", "Stroma", "SAndT"))
PhenoNK$Location <- factor(PhenoNK$Location, levels = c("Tumor", "Stroma", "SAndT"))
PhenoAPC$Location <- factor(PhenoAPC$Location, levels = c("Tumor", "Stroma", "SAndT"))

PhenoAll$PhenoType <- factor(PhenoAll$PhenoType, levels = c("CD4_Eff","CD4_Treg","CD8_Eff","CD8_Treg","B_cells","NK_cells","NKT_cells","M1","M2","CD163","iDC","mDC","pDC","PanCKsingle_TIL", "PanCKsingle_NK","PanCKsingle_APC"))
PhenoAllNoCK$PhenoType <- factor(PhenoAllNoCK$PhenoType, levels = c("CD4_Eff","CD4_Treg","CD8_Eff","CD8_Treg","B_cells","NK_cells","NKT_cells","M1","M2","CD163","iDC","mDC","pDC"))
PhenoTIL$PhenoType <- factor(PhenoTIL$PhenoType, levels = c("CD4_Eff","CD4_Treg","CD8_Eff","CD8_Treg","B_cells","PanCKsingle_TIL"))
PhenoNK$PhenoType <- factor(PhenoNK$PhenoType, levels = c("NK_cells","NKT_cells","M1","M2","CD163","PanCKsingle_NK"))
PhenoAPC$PhenoType <- factor(PhenoAPC$PhenoType, levels = c("iDC","mDC","pDC","PanCKsingle_APC"))

```

```{r}
fig12a <- ggplot(PhenoAllNoCK, aes(x = PhenoType, y = value, fill=giniGroup)) +
    geom_boxplot(outlier.shape = 16, outlier.size = 2, ) +
    theme_classic() +
    # coord_flip() +
    # scale_fill_npg() +
    theme(legend.position = "none") +
    stat_compare_means(paired = NULL, size=2) +
    facet_wrap(~Location, nrow = 3, strip.position = "left", scale="free") +
    scale_y_continuous(expand = c(0,0))


fig12b <- ggplot(PhenoAllNoCK, aes(x = Location, y = value, fill=giniGroup)) +
    geom_boxplot(outlier.shape = 16, outlier.size = 2, ) +
    theme_classic() +
    # coord_flip() +
    # scale_fill_npg() +
    theme(legend.position = "none") +
    stat_compare_means(paired = NULL, size=1.5) +
    facet_wrap(~PhenoType, nrow = 3, strip.position = "left", scale="free")

```



# Fig13 Distance data
```{r}
DistTIL <- read_excel("~/Project/TCR/Distance/BOMI2_dist.xlsx", sheet = "TIL_Gini")
DistTIL <- as.data.frame(DistTIL)
TIL_corrT <- rcorr(as.matrix(DistTIL[,c(5,27:56)]), type = "spearman")
write.csv(TIL_corrT$r, file = "~/Project/TCR/Distance/TIL_corr.csv")
write.csv(TIL_corrT$P, file = "~/Project/TCR/Distance/TIL_corr_Pvalue.csv")

# melt
TIL_corr_melt <- read_excel("~/Project/TCR/Distance/BOMI2_dist.xlsx", sheet = "TIL_Corr")
TIL_corr_melt[,"P_adjust"] <- p.adjust(TIL_corr_melt$`P-Value`,method = "BH")
TIL_corr_melt$label <- NULL
TIL_corr_melt.1d[TIL_corr_melt.1d$var1]
TIL_corr_melt[TIL_corr_melt$P_adjust <= 0.001,"label"] <- "***"
TIL_corr_melt[TIL_corr_melt$P_adjust > 0.001 & TIL_corr_melt$P_adjust <= 0.01,"label"] <- "**"
TIL_corr_melt[TIL_corr_melt$P_adjust > 0.01 & TIL_corr_melt$P_adjust <= 0.05,"label"] <- "*"
TIL_corr_melt$Correlation
```

```{r}
ggplot(TIL_corr_melt, aes(var1, var2, fill = Correlation)) +
    geom_point(shape=21, colour = "black", size = 12)+
    theme_classic()+
    scale_fill_gradientn(colours = rev(brewer.pal(5, "Spectral")))+
    labs(title = "Correlation", x = "Outset", y = "End") +
    theme(axis.text = element_text(size = 10, colour = "black"), axis.title = element_text(size = 10),plot.title = element_text(size=15))+
    geom_text(aes(label = label), size = 5, colour = "black")
```

## 1D plot
```{r}
TIL_corr_melt.1d <- read_excel("~/Project/TCR_Summary/Writing/SumSup.xlsx", sheet = "TIL_Corr_1D")
TIL_corr_melt.1d <- as.data.frame(TIL_corr_melt.1d)
# -0.122419365	0.138273993
#TIL_corr_melt.1d[TIL_corr_melt.1d$var1 == "CD4_Treg" & TIL_corr_melt.1d$var2 == "PanCKsingle", c("Correlation","P-Value")] <- c(-0.122419365, 0.138273993)
TIL_corr_melt.1d[,"P_adjust"] <- p.adjust(TIL_corr_melt.1d$`P-Value`,method = "BH")
TIL_corr_melt.1d[TIL_corr_melt.1d$P_adjust <= 0.001,"label"] <- "***"
TIL_corr_melt.1d[TIL_corr_melt.1d$P_adjust > 0.001 & TIL_corr_melt.1d$P_adjust <= 0.01,"label"] <- "**"
TIL_corr_melt.1d[TIL_corr_melt.1d$P_adjust > 0.01 & TIL_corr_melt.1d$P_adjust <= 0.05,"label"] <- "*"
```

```{r}
ggplot(TIL_corr_melt.1d, aes(var2, var1, fill = Correlation)) +
    geom_point(shape=21, colour = "black", size = 12)+
    theme_classic()+
    scale_fill_gradientn(colours = rev(brewer.pal(5, "Spectral")))+
    labs(title = "Correlation", x = "Outset", y = "End") +
    theme(axis.text = element_text(size = 10, colour = "black"), axis.title = element_text(size = 10),plot.title = element_text(size=15))+
    geom_text(aes(label = label), size = 5, colour = "black")
```




# Fig14 immunetherapy treated cohort
```{r}
library(readxl)
itCohort <- read_excel("~/Project/TCR/ImmuneCohort/GSE126044_gini_ab.xlsx", 
    sheet = "Sheet3")
View(itCohort)

itCohort <- as.data.frame(itCohort)
```

```{r}
f14a <- ggplot(itCohort, aes(x = factor(Responsiveness, level=c('Responder','Non-responder' )), y = Gini, fill=Responsiveness)) +
  geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black", notch = FALSE) +
  theme_classic() +
  #coord_flip() +
  scale_fill_manual(values = c("#00BFC4", "#F8766D")) +
  labs(title = "Responsiveness", x = "Responsiveness", y = "Gini coefficient") +
  theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
  stat_compare_means(paired = NULL)

f14a
```

```{r}
colnames(itCohort)[colnames(itCohort) == "Progression free survival \r\n(month)"] = "Progression free survival (month)"
# Use the cut off from our dataset
itCohort[itCohort$Gini >= 0.19, "giniGroup"] <- "High"
itCohort[itCohort$Gini < 0.19, "giniGroup"] <- "Low"
itCohort$giniGroup = factor(itCohort$giniGroup, levels = c("High", "Low"))

# construct the object
fig14b_test <- survfit(Surv(`Progression free survival (month)`, Status) ~ giniGroup, data = itCohort)

print(fig14b_test)

summary(fig14b_test)$table
```

```{r}
fig14b <- ggsurvplot(fig14b_test,
                        pval = TRUE,
                        palette = c("#e64b35", "#4dbbd5"),
                        break.time.by = 1,
                        ggtheme = theme_classic(),
                        legend.labs = c("High", "Low"),
                        risk.table = "abs_pct",
                        risk.table.y.text = FALSE,
                        xlab = "Time (months)",
                        title = "giniSurv",
                        xlim = c(0,14.5),
                        axes.offset = FALSE,
                        risk.table.fontsize = 2.5,
                        legend = c(0.95,0.97),
                        risk.table.title = "Number at risk by time: n (%)",
                        font.main = c(),
                     break.x.by=3
                     )


fig14b
```

```{r}
surv_median(fig14b_test)
```


# Figure 15 Lymph nodes
```{r}
library(stringr)
GiniClin_tumor$lymph <- str_sub(GiniClin_tumor$`pTNM edt 8 (PMI)`, -2,-1)

GiniClin_tumor[GiniClin_tumor$lymph == "N0", "Lymph_node"] = "N0"
GiniClin_tumor[GiniClin_tumor$lymph == "N1"| GiniClin_tumor$lymph == "N2", "Lymph_node"] = "N1/N2"

ggplot(GiniClin_tumor, aes(x = Lymph_node, y = GiniIndex, fill=Lymph_node)) +
  geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2, colour = "black", notch = FALSE) +
  theme_classic() +
  #coord_flip() +
  #scale_color_npg() +
  labs(title = "lymph", x = "lymph") +
  theme(legend.position = "none",axis.text = element_text(size = 15, colour = "black"), axis.title = element_text(size = 15),plot.title = element_text(size=20)) +
  stat_compare_means(paired = NULL)
```

# Beta clone distance
```{r}
GiniDist <- read.table("/Users/huiyu818/Project/TCR/TCRdist//cloneSummary/beta_tumor_dist_rank.txt")
GiniDist <- as.data.frame(GiniDist)
```

```{r}
pheatmap(GiniDist,
         cluster_rows = F, cluster_cols = F,
         #cutree_cols = 3,
         color = colorRampPalette(brewer.pal(n = 7, name = "RdYlBu"))(100),
         fontsize = 5,
         show_colnames = T
)
```

```{r}
# Load the annotation
GiniDist_anno <-  read_excel("~/Project/TCR/TCRdist/cloneSummary/beta_tumor.xlsx", sheet = "Distance_DF_2")
GiniDist_anno <- as.data.frame(GiniDist_anno)
rownames(GiniDist_anno) <- GiniDist_anno$clone_id_new
```

```{r}
GiniDist_anno_color = list(
#  "GiniIndex" = colorRampPalette(brewer.pal(n = 9, name = "Reds"))(100),
  "Anti_virus" = c(Y = "#D45F51", N = "#B7D2E8"))
```

```{r}
pheatmap(GiniDist,
         cluster_rows = F, cluster_cols = F,
         color = colorRampPalette(brewer.pal(n = 7, name = "RdYlBu"))(100),
         annotation_col = GiniDist_anno["Anti_virus"],
         annotation_colors = GiniDist_anno_color,
         fontsize = 5,
         show_colnames = T
         )
```

```{r}
pheatmap(GiniDist,
         clustering_method = "ward.D2",
         clustering_distance_rows = "euclidean", clustering_distance_cols = "euclidean",
         color = colorRampPalette(brewer.pal(n = 7, name = "RdYlBu"))(100),
         annotation_col = GiniDist_anno["Anti_virus"],
         annotation_colors = GiniDist_anno_color,
         fontsize = 5,
         show_colnames = T
         )
```


```{r}
pheatmap(t(ihcHeatT_tumor_norm2), clustering_method = "ward.D2",
                  clustering_distance_rows = "euclidean", clustering_distance_cols = "euclidean",
                  cutree_cols = 3,
                  #color = colorRampPalette(brewer.pal(n = 7, name = "YlGnBu"))(100),
                  annotation_col = ihcAnno,
                  annotation_colors = ihcAnno_color,
                  fontsize = 5,
                  show_colnames = F
                  )
```


# Cancer testis antigens
```{r}
CTA_list <- read_excel("~/Project/TCR_Summary/Writing/SumSup.xlsx", sheet = "CTA")

CTA_list <- as.data.frame(CTA_list)
```

```{r}
Bomi2RC_tumor_norm <- read.csv("~/Project/Clinical/BOMI2/BOMI2_RNAseq_FPKM/RNASeq_counts_tumor_norm.txt", sep="\t")
```

## CTA list 1 (all from database)
```{r}
library(data.table)
GiniClin_CTA_ori <- as.data.frame(t(Bomi2RC_tumor_norm[CTA_list$Ensgid_1,]))

GiniClin_CTA_ori <- merge(GiniClin_tumor, GiniClin_CTA_ori, by.x = "ID" ,by.y = "row.names")
setnames(GiniClin_CTA_ori, old =  CTA_list[["Ensgid_1"]], new = CTA_list[["Gene_1"]])
```

```{r}
GiniClin_CTA_ori[1,37]
```

```{r}
CTA_corr <- rcorr(as.matrix(GiniClin_CTA_ori[,c(5,37:132)]), type = "spearman")

write.csv(CTA_corr$r, file = "~/Project/TCR/CTA/ctaCor.csv")
write.csv(CTA_corr$P, file = "~/Project/TCR/CTA/ctaPvalue.csv")
```

```{r}
CTA_corrT <- read_excel("~/Project/TCR/CTA/CTA.xlsx", sheet = "Sheet3")

CTA_corrT <- as.data.frame(CTA_corrT)

CTA_corrT <- rename(CTA_corrT, c("GeneSymbol"="...1"))
CTA_corrT[,"padj"] <- p.adjust(CTA_corrT$`P-value`,method = "BH")

write.csv(CTA_corrT, file = "~/Project/TCR/CTA/ctaPadj.csv")
```

```{r}
CTA_corrT[CTA_corrT$padj < 0.05,]
```

## CTA list 2 (noval list)
```{r}
GiniClin_CTA_ori_2 <- as.data.frame(t(Bomi2RC_tumor_norm[CTA_list[!is.na(CTA_list$Ensgid_2),"Ensgid_2"],]))

GiniClin_CTA_ori_2 <- merge(GiniClin_tumor, GiniClin_CTA_ori_2, by.x = "ID" ,by.y = "row.names")
setnames(GiniClin_CTA_ori_2, old =  CTA_list[!is.na(CTA_list$Ensgid_2),"Ensgid_2"], new = CTA_list[!is.na(CTA_list$Ensgid_2),"Gene_2"])
```

```{r}
GiniClin_CTA_ori_2[3,37]
```

```{r}
CTA_corr_2 <- rcorr(as.matrix(GiniClin_CTA_ori_2[,c(5,37:126)]), type = "spearman")

write.csv(CTA_corr_2$r, file = "~/Project/TCR/CTA/ctaCor_list2.csv")
write.csv(CTA_corr_2$P, file = "~/Project/TCR/CTA/ctaPvalue_list2.csv")
```

```{r}
CTA_corrT_2 <- read_excel("~/Project/TCR/CTA/CTA.xlsx", sheet = "GeneList2")
CTA_corrT_2 <- as.data.frame(CTA_corrT_2)

CTA_corrT_2[,"padj"] <- p.adjust(CTA_corrT_2$Pvalue,method = "BH")
```


## Vocanol plot
```{r}
ggplot(data = diffGene, aes(x = log2FoldChange, y = -log10(padj), color = sign)) + 
  geom_point(size = 1) +  #Create scatter plot
  scale_color_manual(values = c('red', 'gray', 'green'), limits = c('UP', 'none', 'DOWN')) +  #Set the color
  labs(x = 'log2 Fold Change', y = '-log10 adjust p-value', title = 'High clonality vs Low clonality', color = '') +  #Set the labels
  theme(plot.title = element_text(hjust = 0.5, size = 14), panel.grid = element_blank(), #Change the them and grid lines
  panel.background = element_rect(color = 'black', fill = 'transparent'), 
  legend.key = element_rect(fill = 'transparent')) +
  geom_vline(xintercept = c(-1, 1), lty = 3, color = 'black') +  #Add threshold line
  geom_hline(yintercept = 2, lty = 3, color = 'black') +
  xlim(-12, 12) + ylim(0, 35) + #Set the curve boundary
  geom_text_repel(
    data = diffGene,
    aes(label = label),
    size = 3, segment.color = "blue", show.legned = FALSE,
    point.padding = unit(0.8, "lines")
  )
```


# ISS plot
```{r}
ISS_dot_data <- read_excel("~/Project/TCR/ISSimmunopanels/Analysis/DotPlot sample.xlsx")
View(ISS_dot_data)

ISS_dot_data <- as.data.frame(ISS_dot_data)
```

```{r}
ggplot(ISS_dot_data, aes(Clone, EPCAM_CDH1_anno)) +
  geom_point(aes(size=Clone_fraction, color = Clone_mean))+
  facet_wrap(~ Sample, scales = "free_x", drop = TRUE) +
  theme_bw()+
  theme(panel.grid.major = element_blank(),  # Remove major grid lines
        panel.grid.minor = element_blank()) + # Remove minor grid lines
  scale_size(range = c(1, 10)) +
  scale_color_gradientn(colors = rev(brewer.pal(5, "Spectral")))+
  labs(title = "Bubble plot grouped by sample", x = "Clone", y = "EPCAM_CDH1 annotation") +
  theme(axis.text.x = element_text(size = 10, colour = "black", angle = 45), axis.title = element_text(size = 10),plot.title = element_text(size=15))
  #geom_text(aes(label = label), size = 5, colour = "black")
```

```{r}
library(dplyr)

ISS_dot_data <- ISS_dot_data %>%
  group_by(Sample) %>%
  mutate(Normalized_Clone_fraction = scale(Clone_fraction),  # Normalizing clone fraction
         Normalized_Clone_mean = scale(Clone_mean)) %>%  # Normalizing clone mean
  ungroup()
```


```{r}
ISS_dot_data_L766 <- ISS_dot_data[ISS_dot_data$Sample == "L766T",]
```

```{r}
ggplot(ISS_dot_data_L766, aes(Clone, EPCAM_CDH1_anno)) +
  geom_point(aes(size=Clone_fraction, color = Clone_mean))+
  theme_bw()+
  theme(panel.grid.major = element_blank(),  # Remove major grid lines
        panel.grid.minor = element_blank()) + # Remove minor grid lines
  scale_color_gradientn(colors = rev(brewer.pal(5, "Spectral")))+
  labs(title = "Bubble plot grouped by sample", x = "Clone", y = "EPCAM_CDH1 annotation") +
  theme(axis.text.x = element_text(size = 10, colour = "black", angle = 45), axis.title = element_text(size = 10),plot.title = element_text(size=15))
```

```{r}
ISS_dot_data_L596 <- ISS_dot_data[ISS_dot_data$Sample == "L596T",]
```

```{r}
ggplot(ISS_dot_data_L596, aes(Clone, EPCAM_CDH1_anno)) +
  geom_point(aes(size=Clone_fraction, color = Clone_mean))+
  theme_bw()+
  theme(panel.grid.major = element_blank(),  # Remove major grid lines
        panel.grid.minor = element_blank()) + # Remove minor grid lines
  scale_color_gradientn(colors = rev(brewer.pal(5, "Spectral")))+
  scale_size(range = c(1, 10)) +
  labs(title = "Bubble plot grouped by sample", x = "Clone", y = "EPCAM_CDH1 annotation") +
  theme(axis.text.x = element_text(size = 10, colour = "black", angle = 45), axis.title = element_text(size = 10),plot.title = element_text(size=15))
```










